green energy for carbon neutral ecosystem essay in malayalam

എന്താണ് ഹരിതഗൃഹപ്രഭാവം?

ഭൂ മിയുടെ അന്തരീക്ഷത്തിലെ ചില ഘടകങ്ങൾ (പ്രധാനമായും കാർബൺ ഡൈഓക്സൈഡ്) സൗരതാപത്തെ ആഗിരണം ചെയ്യുകയും ഭൂമിയുടെ താപനില ഉയർത്തുകയും ചെയ്യുന്ന പ്രതിഭാസമാണ് ഹരിതഗൃഹപ്രഭാവം. (Greenhouse effect). ഭൂമി തണുത്തുറഞ്ഞു പോകാതെ ജീവ​െൻറ നിലനിൽപിന് അനുയോജ്യമായ താപനില നിലനിർത്താൻ ഹരിതഗൃഹപ്രഭാവം സഹായിക്കുന്നു. എന്നാൽ, ഇപ്പോൾ മനുഷ്യ​െൻറ തെറ്റായ ചില പ്രവർത്തനങ്ങൾമൂലം അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവ് കൂടുകയും ഭൂമിയുടെ അന്തരീക്ഷ താപനില അനഭിലഷണീയമാം വിധം ഉയരുകയും ചെയ്യുന്നുണ്ട്. പരിസ്ഥിതി ശാസ്ത്രജ്ഞർക്കിടയിൽ ഹരിതഗൃഹപ്രഭാവം ഒരു സജീവ ചർച്ചാവിഷയമാകുന്നത് അതു കൊണ്ടാണ്.

പേരിനു പിന്നിൽ

ശൈത്യരാജ്യങ്ങളിൽ അതിശൈത്യം കാരണം ചിലയിനം ചെടികൾ നശിച്ചുപോവുക സാധാരണമാണ്. അതിനാൽ, കർഷകർ സംരക്ഷിക്കേണ്ട ചെടികളെ ഒരു ചില്ലു കൂടിനുള്ളിൽ വളർത്തുന്നു. ഈ ചില്ലുകൂടാണ് ഹരിതഗൃഹം (Green house). ചില്ല് സുതാര്യമായതിനാൽ പ്രകാശരശ്മികൾ ഉള്ളിൽ കയറും. എന്നാൽ, ചില്ലുകൂട് താപരശ്മികളെ പുറത്തുകടക്കാൻ അനുവദിക്കാതെ കെണിയിലാക്കുന്നു. അതിനാൽ, ചില്ലുകൂടിനുള്ളിലെ താപനില ഉയരുകയും ചെടികൾ അതിശൈത്യത്തിൽ നിന്നും രക്ഷപ്പെടുകയും ചെയ്യുന്നു. ഇത്തരം ചില്ലുകൂടുകൾ ചെയ്യുന്നതുപോലെ അന്തരീക്ഷത്തിലെ ചില വാതകങ്ങൾ ഭൂമി പ്രതിപതിപ്പിക്കുന്ന താപവികിരണങ്ങൾ ശൂന്യാകാശത്തിലേക്ക് നഷ്​ടപ്പെടാതെ തടയുന്നു. ഇതാണ് ഹരിതഗൃഹപ്രഭാവം.

അന്തരീക്ഷം ചൂടുപിടിക്കുന്നതെങ്ങനെ?

ഭൂമിയുടെ അന്തരീക്ഷതാപനില ഉയരുന്നത് ഭൂമിയിൽ നേരിട്ടുപതിക്കുന്ന സൂര്യരശ്​മികളാലല്ല. അവ ​ ഹ്രസ്വതരംഗങ്ങളായതിനാൽ അധികം വായുകണങ്ങളുമായി സമ്പർക്കത്തിൽ വരാത്തതാണ് കാരണം. എന്നാൽ, ഇവയേറ്റ് ചൂടുപിടിക്കുന്ന ഭൂമി, ഇൻഫ്രാറെഡ് വികിരണങ്ങൾ (ഉഷ്ണരശ്മികൾ) ഭൗമോപരിതലത്തിൽ നിന്നും പ്രതിപതിപ്പിക്കും. തരംഗദൈർഘ്യം കൂടുതലുള്ളതിനാൽ ഇവ വളഞ്ഞുപുളഞ്ഞ് സഞ്ചരിച്ച് അന്തരീക്ഷത്തിലെ കൂടുതൽ വായുകണങ്ങളുമായി സമ്പർക്കത്തിൽ വന്ന് അവയെ ചൂടുപിടിക്കും. ഇതാണ് അന്തരീക്ഷതാപനില ഉയർത്തുന്നത് (ഇൻറർലോക്കിട്ട മുറ്റമുള്ള വീടുകളിൽ നേരിട്ട് സൂര്യപ്രകാശമേൽക്കാത്ത സിറ്റൗട്ടിൽ ഇരിക്കുമ്പോൾ നമുക്ക് അത്യധികമായ ചൂട് അനുഭവപ്പെടാനുള്ള കാരണം ഈ ഭൗമവികിരണങ്ങളാണ്).

താപത്തെ കെണിയിലാക്കുന്നവർ

ഭൗമോപരിതലത്തിൽനിന്നും പ്രതിപതിക്കുന്ന താപവികിരണങ്ങളാണ് അന്തരീക്ഷത്തെ ചൂടുപിടിപ്പിക്കുന്നത് എന്നു നാം കണ്ടു. ഈ താപവികിരണങ്ങളെ ബഹിരാകാശത്തേക്ക് തിരിച്ചുപോകാൻ അനുവദിക്കാതെ അന്തരീക്ഷത്തിലെ ചില വാതകങ്ങൾ ആഗിരണം ചെയ്യുന്നു. ഇത്തരം വാതകങ്ങളാണ് ഹരിതഗൃഹവാതകങ്ങൾ (Green house gases). കാർബൺ ഡൈഓക്സൈഡ്, മീഥൈൻ, നൈട്രസ് ഓക്സൈഡ്, നീരാവി എന്നിവയാണ്പ്രധാന ഹരിതഗൃഹവാതകങ്ങൾ. ഈ വാതകങ്ങൾ അവ ആഗിരണം ചെയ്ത താപവികിരണങ്ങളെ വീണ്ടും താഴേക്കും മുകളിലേക്കും വശങ്ങളിലേക്കും പ്രതിപതിപ്പിക്കുന്നു. ഇവയെ ഭൗമോപരിതലവും മറ്റു ഹരിതഗൃഹ വാതകകണങ്ങളും വീണ്ടും ആഗിരണം ചെയ്യുകയും പുറത്തുവിടുകയും ചെയ്യുന്നു. ഈ ചാക്രിക പ്രക്രിയയാണ് അന്തരീക്ഷത്തെ ജീവ​ന്‍റെ നിലനിൽപിന് അനുയോജ്യമാം വിധം ചൂടുള്ളതാക്കുന്നത്.

അമിതമായാൽ അമൃതും വിഷം

സസ്യ-ജന്തുജാലങ്ങളുടെ നിലനിൽപിന് ആവശ്യമായ അളവിൽ അന്തരീക്ഷതാപം നിലനിർത്തുന്നത് ഹരിതഗൃഹവാതകങ്ങളിൽ പ്രധാനപ്പെട്ട കാർബൺ ഡൈഓക്സൈഡാണ്. എന്നാൽ, കഴിഞ്ഞ ഏതാനും പതിറ്റാണ്ടുകളായി കാർബൺ ഡൈഓക്സൈഡി​െൻറ അനുപാതം അന്തരീക്ഷത്തിൽ സാരമായി വർധിച്ചിരിക്കുകയാണ്. കൽക്കരി, പെട്രോളിയം, പ്രകൃതിവാതകം എന്നിവയുടെ അമിതമായ ഉപയോഗം, വനനശീകരണം എന്നിവയാണ് അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവു കൂടാൻ കാരണമായത്.പത്തൊമ്പതാം നൂറ്റാണ്ടിൽ അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവ് 280 പി.പി.എം ആയിരുന്നു (Parts per million അഥവാ പത്ത് ലക്ഷത്തിൽ ഒരംശം എന്നതാണ് പി.പി.എം കൊണ്ട് ഉദ്ദേശിക്കുന്നത്). ഇന്ന് അത് 350 പി.പി.എം ആണ്.

അന്തരീക്ഷത്തിൽ ഉണ്ടായിട്ടുള്ള കാർബൺ ഡൈഓക്സൈഡ് വർധനയുടെ 25 ശതമാനവും സംഭവിച്ചിട്ടുള്ളത് കഴിഞ്ഞ 40 വർഷങ്ങൾക്കുള്ളിലാണ്. ഈ നില തുടർന്നാൽ 2050ൽ കാർബൺ ഡൈഓക്സൈഡി​ന്‍റെ അളവ് 600 പി.പി.എം ആകും. ഇത് ആഗോളതാപനത്തിന് ഇടയാക്കും.

ഹരിതഗൃഹവാതകങ്ങളുടെ അളവു കൂടുന്നതുമൂലം അന്തരീക്ഷതാപനില ഉയരുന്നതാണ് ആഗോളതാപനം(Global warming). ഇരുപതാം നൂറ്റാണ്ടി​െൻറ രണ്ടാം പാതിയിൽ ഭൂമിയുടെ ശരാശരി താപ നില 0.8ഡിഗ്രി C മുതൽ 1.2ഡിഗ്രി Cവരെ ഉയർന്നു എന്നാണ് വിവിധ പഠനങ്ങൾ ചൂണ്ടിക്കാണിക്കുന്നത്. 1986 നും 2005നും ഇടയിൽ ഉണ്ടായ തോതിൽ അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡ് ഇനിയും എത്തിയാൽ 2100 ആകുമ്പോഴേക്കും ഭൂമിയുടെ ശരാശരി താപനില 5.8ഡിഗ്രി Cവരെ ഉയരുമെന്നാണ് കാലാവസ്ഥാ വ്യതിയാനത്തെക്കുറിച്ച് പഠനം നടത്തുന്ന ശാസ്ത്രജ്ഞരുടെ സംഘമായ IPCC (Intergovernmental Panel on Climate Change) മുന്നറിയിപ്പ് നൽകുന്നത്.

ഭൂമിയുടെ ഇപ്പോഴത്തെ ശരാശരി താപനില 15ഡിഗ്രി C മാത്രമാണെന്നിരിക്കെ ഈ വർധന എത്രമാത്രം ഭീതിദമാണ്! ഇതു പല പ്രശ്നങ്ങളും ഭൂമിയിൽ സൃഷ്​ടിക്കും. അൻറാർട്ടിക്കയിലെയും ഹിമാലയത്തിലെയും മഞ്ഞുരുകി ഇന്ത്യയിലെ മുംബൈ അടക്കം ലോകത്തെ പല വൻനഗരങ്ങളും ചില ദ്വീപരാജ്യങ്ങളും വെള്ളത്തിനടിയിലാകും. ഭൂമിയിലെ ഋതുഭേദങ്ങൾ മാറിമറിയും. ചിലയിടത്ത് പേമാരിയും ചിലയിടത്ത് വരൾച്ചയുമുണ്ടാകും. കൊടുങ്കാറ്റുകളും ചുഴലിക്കാറ്റുകളും സാർവത്രികമാകും. ഭൂമിയിലെ വിവിധ ആവാസ വ്യവസ്ഥകൾ നശിക്കും. കൃഷിനാശവും അതു വഴി ഭക്ഷ്യക്ഷാമവുമുണ്ടാകും. മനുഷ്യർക്ക് ത്വക് അർബുദം പോലുള്ള രോഗങ്ങളുണ്ടാകും.

ആഗോളതാപനത്തി​െൻറ ഗൗരവം ഉൾക്കൊണ്ടാണ് പല വർഷങ്ങളുടെയും ലോകപരിസ്ഥിതിദിനസന്ദേശങ്ങൾ പോലും രൂപപ്പെട്ടത്. മഞ്ഞുരുകൽ ഒരു ചൂടുള്ള വിഷയം, 'CO2- Kick the habit', 'Beat air pollution', 'ആഗോള താപനം - മരമാണ് മറുപടി' തുടങ്ങിയവ ഉദാഹരണങ്ങളാണ്.

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The Kerala State Action Plan on Climate Change -  Special focus on six areas

Kerala has released a revised action plan to deal with the impacts of climate change. Kerala State Action Plan on Climate Change 2023 - 2030 prepared by the Directorate of Environment and Climate Change, gives clarity about the projects to be implemented in the state and the areas that need special attention.

The action plan is being implemented with a special focus on agriculture, animal husbandry, fisheries, forest and biological ecosystems, health, and water resources. Areas that are most vulnerable due to climate change will be identified and appropriate measures will be implemented. According to the action plan Wayanad, Kozhikode, Kasaragod, Palakkad, Alappuzha, Idukki, Kannur, Malappuram, and Kollam are in the ‘high’ vulnerability class, whereas Thiruvananthapuram and Kottayam are in the ‘medium’ vulnerability class and Thrissur, Ernakulam, and Pathanamthitta are in the ‘low’ vulnerability category. Inadequate irrigation system and poor groundwater/surface water quality are explained in the action plan and measures to resolve such issues are also included.

The action plan envisages prudent carbon emission reduction. Kerala is the fifth lowest greenhouse gas emitting state in India. Climate change mitigation can be achieved by reducing greenhouse gas emissions or increasing carbon sequestration through various means. Power generation, transportation, industry, energy-intensive agriculture, and buildings are major sources of carbon emissions in the state.

The action plan envisages mitigation strategies for avoiding approximately 57,000 ktCO2 from various sectors by 2030. The proposed mitigation strategies for the State will require an outlay of ₹52,238 crores. The project aims to establish renewable electricity projects, improve energy efficiency in the industrial sector, improve transmission and distribution facilities, promote electric vehicles, etc.

Important mitigation measures for 2030 include; increasing renewable energy-installed capacity to 3.46 GW, energy efficiency in homes will be 53%, a higher share of electric vehicles in public transport, and adopting solar-based and energy-efficient strategies. The action plan also envisages important interventions in the areas like land-use planning, sustainable coastal protection, rehabilitation of vulnerable communities, development of sustainable drainage systems for flood prevention, integrated coastal management, climate change monitoring system, climate awareness, and climate education.

Kerala is implementing comprehensive and sustainable development concepts towards transitioning to a renewable energy-based and net carbon-neutral state. Kerala is the first state to include hydrogen-powered mobility in the zero-emission mobility policy. The Kerala Hydrogen Economy Mission has been formed to ensure policy formulation and collaboration to make the state a green hydrogen hub. Green hydrogen production is critical for the state's efforts to achieve carbon neutrality by 2050. Kerala's selection as the second-best state in NITI Aayog's State Energy Climate Index is a recognition of its efforts towards carbon neutrality. Kerala has also performed well in indicators related to clean energy initiatives, energy efficiency, and environmental sustainability.

Kerala will soon rollout an ESG-focused industrial policy that will promote environmental-social-governance-oriented investments which aimed at promoting responsible industrial production. Steps are in progress to start a climate-smart coffee programme at Wayanad which includes Agro-Forestry, Energy, and Climate Efficiency Centre. Citizen Climate Connect will be implemented in Thrissur and Palakkad to document how the public observes and assesses climate change. An Electric Vehicle Consortium program is also underway with the aim of reducing carbon emissions and promoting e-mobility.

Due to its geographical features, Kerala is highly vulnerable to natural disasters. In view of this, the construction of disaster-resilient infrastructure and buildings, strengthening of disaster response systems and enabling individuals and society to adapt to the long-term impacts of climate change will be carried out scientifically and timely in the state based on detailed studies.

Last updated on : 2022-12-13 17:12:20

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കാര്‍ബണ്‍ ന്യൂട്രല്‍: ഇതാണ് ഇന്ത്യ പഠിക്കുന്ന 'മീനങ്ങാടി മോഡല്‍'

അന്തരീക്ഷത്തിലേക്ക് തള്ളുന്ന കാര്‍ബൺ ഡയോക്സൈഡിന്‍റെ അതേ അളവ് കാര്‍ബൺ ഡയോക്സൈഡ് ഒഴിവാക്കാനായാൽ ഒരു പ്രദേശം കാര്‍ബൺ ന്യൂട്രൽ ആയെന്ന് പറയാം. കാലാവസ്ഥാ വ്യതിയാനം ചെറുക്കാൻ ലോകം മുഴുവൻ കാ‍ര്‍ബൺ പുറംതള്ളൽ കുറയ്ക്കാൻ ശ്രമിക്കുമ്പോൾ, വയനാട് മീനങ്ങാടി ഒരു മാതൃകയാണ്. ഇപ്പോള്‍ ഇന്ത്യയിലെ മറ്റു സ്ഥലങ്ങളും അത് പരീക്ഷിക്കുകയാണ്.

വളണ്ടിയര്‍ സര്‍വ്വെയുടെ ഭാഗമായി മരം പരിശോധിക്കുന്നു-HANDOUT/Samayam Plus

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വയനാട് മീനങ്ങാടിയിൽ ജിയോടാഗ് ചെയ്ത ഒരു മരം -HANDOUT/Samayam Plus

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കെ.ഇ വിനയൻ, മീനങ്ങാടി പഞ്ചായത്ത് പ്രസിഡന്‍റ്‍ -HANDOUT/Samayam Plus

ഗിരിജൻ ഗോപി, സീനിയർ ഡെവലപ്‍മെന്‍റ് കോർഡേനിറ്റേർ, എം.എസ് സ്വാമിനാഥന്‍ റിസര്‍ച്ച് സെന്‍റര്‍

• ട്രക്കിങ് ദുരന്തം
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ആര്‍ട്ടിക്കിള്‍ ഷോ

Climate Change

Making of a carbon-neutral community in kerala.

A pilot project in Kerala’s Wayanad district could be a model for the rest of the country

By Nidhin Davis K

Published: tuesday 13 march 2018.

The challenge of climate change calls for extraordinary vision, leadership, compassion and wisdom. The accumulation of greenhouse gases (GHGS) in the atmosphere has led to global warming. If the world is indeed serious about making climate-friendly investments, it must consider the opportunity provided by a country like India where economic growth could be achieved with minimum level of emission by employing new technologies and finance. Having said that, to achieve India’s ambitious Intended Nationally Determined Contributions (INDCS) as per the Paris Agreement, it is crucial that our future development activities follow a “Carbon-Neutral” trajectory. Carbon neutrality stands for “net zero emission” of GHGS from anticipated anthropogenic activities. Post-Paris climate commitments, Thomas Isaac, Finance Minister of Kerala and a well-known economist, wanted to set an example in the state for the rest of the country to follow. Wayanad district—nestled in the Western Ghats on the extension of Deccan Plateau and home to crops that are thermo-sensitive and vulnerable to climate change such as coffee, paddy and pepper—was selected for a community-based climate change adaptation initiative called “Carbon Neutral Wayanad”. A pilot project was then launched at a local government level on June 5, 2016; Meenangadi gram panchayat leadership came forward to take up the responsibility to be a model panchayat for Wayanad district and the rest of the country. Non-profit Thanal Trust was entrusted by the Kerala government to provide technical assistance to this flagship project. GHG emission and carbon stock estimation methodology At present, there is no established research methodology for assessing carbon status at a regional scale. The Ministry of Environment, Forest and Climate Change (MOEF&CC) prepares National Inventory on ghg emissions to report to unfccc. Similarly, “GHG Platform India”— a consortium of research ngos—reports GHG emissions at state level based on secondary data. The Carbon Neutral Technical Cell, comprising representatives of local government, research scholars and technical experts from Thanal, has followed the ipcc 2006 guidelines, Greenhouse Gas Protocol and the UNEP low-carbon mobility toolkit for establishing a research methodology to assess carbon status at a regional scale. Emission inventory covers the sectors of Agriculture Forestry and Other Land use (AFOLU), Livestock, Energy, Transportation and Waste for the base year 2016. Industry sector is not considered since there is no predominant industry in the district. Ward-based socioeconomic sampling and geographical sampling was done. Primary data along with secondary data from the statistics department was used for calculating emissions from livestock and AFOLU sector. UNEP low-carbon mobility toolkit was used for estimating emissions from transportation sector, which is a major contributor of GHG emissions in this region. Region-specific emission factor for southern grid was used for calculating emissions from Electricity consumption—the single major contributor of emissions.

Existing anaerobic solid waste management in landfills and burning of non-biodegradable waste contribute towards methane emissions. Similarly, methane emissions due to enteric fermentation and manure management in livestock sector were also estimated using country-specific emission factor and livestock statistics data. There’s an increasing trend in the share of livestock in the local economy because farmers depend largely on this relatively safer sector as compared to plantation crops, which are highly vulnerable to climate change. Emissions in the form of methane and other GHGS are then converted into carbon dioxide equivalents. Simultaneously, carbon stock in different sectors was calculated using a standard procedure for the base year 2016. Organic carbon stored in soil was estimated by collaborating with the Department of Soil Survey & Soil Conservation. Quantitative analysis of soil samples collected from all over the panchayat was done by using laboratory methods. Forests and plantations cover 50 per cent of total geographical area and they act as a major carbon sink. Annual carbon sequestration in forests and coffee plantations were quantified by multiplying the area of land under each category with the sequestration factor (in kg/ha). To calculate the quantity of carbon sequestration in trees in the homesteads, a household-level tree survey was carried out along with an allometric equation to calculate the AGB (Above Ground Biomass). Following this exercise, the stakeholders of the project arrived at baseline estimation of carbon status of the region—14,739 tonnes of GHG emissions (carbon dioxide equivalent). A comprehensive Carbon Neutral Action Plan will be proposed with sector-specific short-term and long-term interventions to offset the emissions as well as to conserve existing carbon sink. Livelihood need assessment

The article was first published in the State of India's Environment Report 2018 . RESOURCES

• UNEP low-carbon mobility toolkit, 2016
• Guidelines on National Greenhouse Gas Inventories, 2006; IPCC

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Original research article, the impact of renewable energy on carbon neutrality for the sustainable environment: role of green finance and technology innovations.

• 1 School of Economics and Management, Beijing University of Technology, Beijing, China
• 2 School of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Over time environmental degradation has become a severe concern globally, especially in China. Therefore, to solve this serious issue, environmental economists have tried their best to explain the crux of environmental degradation. Besides such efforts, they have not reached a single opinion. Nowadays, economic progress has been considered a primary target to compete with other nations at any cost of environmental degradation. But, there may be some alternative solutions to reduce such costs, and the existing literature has not considered such core indicators. Thus, the present study assesses the eco-friendly variables for a sustainable environment in which green finance, green energy, and research and development expenditures. Moreover, this study also focuses on socio-economic factors like economic growth, urbanization, and openness. Time series estimators such as fully modified ordinary least square and Markov switching regression model are employed to investigate selected variables’ long-run impact on carbon emissions. The estimated outcomes show the significant contribution of green finance, green energy, openness, and R&D expenditures to environmental quality. Likewise, China’s urbanization and economic progress are harmful to environmental quality. Moreover, this study investigates the causal association between the selected variables and shows the two-way causal association between openness and emissions, RE and openness, and green finance and R&D expenditures. The results of the uni-directional association are also interesting. The current study develops some interesting policy implications for a sustainable environment on behalf of empirical outcomes.

1 Introduction

It is possible that reaching carbon neutrality, a state in which the amount of carbon dioxide released into the atmosphere is equal to the amount removed. It will emerge as the most important global environmental objective of the 21st century. Many nations have publicly stated their intention to achieve carbon neutrality in response to growing concerns regarding the impact that rising atmospheric concentrations of carbon dioxide (CO2) and other greenhouse gases will have on the world’s climate (see here for more information). The United Kingdom made a commitment in 2019 to reach this objective, which is also referred to as having net-zero carbon emissions by the year 2050. It was then followed in 2020 by Japan and in 2060 by China, whose target year is 2060 ( Gang Cheng et al., 2021 ). In the latter half of the year 2020, the Secretary-General of the United Nations issued a call to action to all of the world’s governments, urging them to declare a Climate Emergency in their countries until carbon neutrality is reached (S. A. R. Khan et al., 2021c ; Shan et al., 2021 ).

On 12 December 2015, a landmark UN climate agreement was signed in Paris by all countries in response to the ever-increasing global greenhouse effect in order to combat climate change. This agreement was to take effect immediately ( Tao et al., 2021 ). As part of the Paris agreement of 2015, all nations committed to limiting warming to less than 2.0°C and making efforts to bring it down to less than 1.5°C through the accomplishment of carbon reduction around the year 2050. ( Ma et al., 2022 ). The pre-industrial temperature was 1.2°C lower than the global average temperature in 2020, and the effects of this warming can be felt worldwide ( Ji et al., 2021a ). According to the most recent climate data, there is an immediate need to step up our efforts to cut down on the amount of greenhouse gases in the atmosphere in order to stop the progression of global warming (S. A. R. Khan et al., 2021d ).

It is of the utmost importance to reduce carbon emissions from fossil fuels and food while simultaneously encouraging carbon sequestration in terrestrial and marine ecosystems if we are going to achieve carbon neutrality and support human activities in a sustainable manner ( Safi et al., 2021 ) ( Latif et al., 2021 ). Reducing carbon emissions to net-zero is difficult to achieve because of the magnitude of the fluxes involved, even though various countries have devised various strategic routes to achieve carbon neutrality ( Dong et al., 2022 ) ( Huang et al., 2022 ; Iqbal et al., 2019b , 2019a ). According to the International Energy Agency, the world will not be able to reach its goal of becoming carbon neutral by 2050 unless new crude oil, natural gas, and Coal are not extracted or developed after 2021 ( Qiang Ma et al., 2021 ). In this regard, research into and adoption of renewable energy from carbon-free sources (wind, tide, sunlight and so on) and biomass is the key to bridging the gap between the rhetoric and the reality of net-zero CO2 emissions (S. A. R. Khan et al., 2021a ).

Every nation on Earth has a responsibility to act consistently with its endowment of energy resources and the employment of technologies that promote social equity while having a minimal negative impact on nature ( Chien et al., 2021 ) ( Hou et al., 2019 ; Yumei et al., 2021 ; Yaming Zhang et al., 2022 ). However, the intolerable degradation of the natural environment caused by fossil fuels can only be mitigated by decoupling the demand for energy from the economy’s growth and by decreasing the use of fossil fuels ( Yu and Zhang, 2022 ). Emerging economies face a number of challenges as a result of their growing financial and economic requirements. This is because an increase in economic activity simultaneously increases the energy demand, primarily from conventional sources such as gas, coal, oil, and so on ( Dong et al., 2022 ). A strategic commodity to achieve sustainable development is renewable energy (RE) ( Guo et al., 2022 ). It is assumed that various renewable energy sources, such as solar, wind, waste, and biomass, are cost-effective and environmentally friendly because they reduce pollution, offer better security to the energy, lessen the harmful effects of climate change, and finally provide low-cost electricity to remote areas ( Ma et al., 2022 ). However, most of the research ( Huang et al., 2021 ) on renewable energy’s impact on the environment has come to optimistic conclusions (S. A. R. Khan et al., 2021d ).

According to the endogenous growth theory, technological advancements that come about as a result of investments in research and development (R&D) can result in increased production efficiency and efficiency in the use of natural resources and energy. Because they can better afford investments in research and development ( Hou et al., 2019 ; Mohsin et al., 2021 ; Rao et al., 2022 ; Yumei et al., 2021 ; Yaming Zhang et al., 2022 , Yaming Zhang et al., 2021 ), countries are better able to adopt efficient technologies as their incomes grow. More efficient technologies lead to a cleaner environment because they reduce the load placed on natural resources and cut down on the amount of emissions and waste produced as byproducts ( Ya Cheng et al., 2021 ; Shao et al., 2021 ). For example, increasing investments in research and development can improve environmental quality under conditions in which effective environmental management systems are in place to ensure appropriate waste management ( Huang et al., 2021 ).

The impact that advances in technology will have on carbon emissions, on the other hand, is a priori a matter of conjecture. Research and development may have a negative impact on environmental quality through the scale effects of a larger production, which are associated with higher growth and trade openness even though R&D has positive effects on economic growth and trade (see this link for more information). Even though advancements in technology may make it possible to increase productivity, doing so may still require the application of a greater quantity of natural resources, which may increase CO2 emissions ( Khan et al., 2022a ). This possibility is strengthened by the fact that the returns on investment in R&D tend to decrease over time. Because it gets harder to make discoveries as the amount of previously accumulated knowledge grows, the amount of induced research and development gradually decreases over time (S. A. R. Khan et al., 2021e ; Mehmood, 2021 ). Despite this, there is an ongoing requirement for additional inputs of natural resources to support the economy’s expansion.

To promote social development, finance is an important factor in coordinating and allocating resources in various fields ( Akadiri et al., 2021 ). Still, it is also a major driver of the increase in carbon emissions, which limits regional green development to a certain extent. There is a large funding gap for local green development because green projects generally have strong positive externalities that benefit the economy and society. Still, the financial industry’s profits do not increase because of these positive externalities. Environmentally friendly projects benefit both the economy and society because capital is profit-seeking. As a result, new financial instruments and policies are required to boost investment, improve financing’s environmental benefits, and achieve sustainable development objectives. Green Finance refers to a wide range of policies and new financial instruments, including green funds, green bonds, green banks, fiscal policies, carbon trading markets, and fin-tech. As a distinct form of finance, green finance aims to transfer environmental risks and improve environmental quality. In addition to environmental and sustainable finance, the term green finance is used. Significant progress has been made in the financial sector, which is critical to ensuring that growth in the economy does not adversely affect the natural environment (S. A. R. Khan et al., 2021b ).

The following are some of how this paper adds to the body of previously published research: (1) It is assumed that this will be a pioneering effort to analyze the relationship between carbon dioxide emissions and renewable energy, green finance, and research and development expenditures (R&D) to mitigate global warming using the annual data from the Chinese economy. This will consider the significant role economic progress, openness, and urbanization play in the equation. (2) The FMOLS and MS-Regression models are used in this study to validate long-run association aimed variables to analyze the impact of RE on the mitigation of global warming. The carbon neutrality goal aims to achieve a level of net-zero co2 emission by the mid-21st century. It has been suggested by over 130 governments and regions. This target has been referred to as the net-zero carbon emissions target. It is a well-known fact that lowering emissions of greenhouse gases is essential to the sustainable growth of the world. It is also the case that governments are obligated to continuously foster economic expansion and work toward raising the general level of people’s living standards. Renewable energy is widely available and has a tremendous growth potential; as a result, it protects against disruptions in the energy supply and helps alleviate energy shortages. However, one must also keep in mind that the role of RE in environmental sustainability varies from study to study, which is something that must not be forgotten. As a result, the majority of the research done in this day and age has concentrated on the pattern of utilization of renewable energy. In a similar vein, China has made significant progress in developing green energy and has attempted to increase the use of renewable energy. In order to realize the goal of a carbon-free economy or carbon neutrality, it is essential to cultivate the right mentality of enhancing economic activities while simultaneously working toward the creation of a clean environment. According to Qin, (2021) , a greater use of non-renewable energy sources in economic activities, such as Coal, crude oil, and natural gas, rather than renewable energy may result in environmental degradation by increasing carbon emissions. This could have a negative impact on the quality of the environment. In addition, Chishti and Sinha, (2022) argued that using renewable resources is beneficial to the environment because it lowers levels of carbon dioxide emission. In contrast, fossil fuels and non-renewable resources have negative effects on the environment. Although our paper is part of a large body of research done in the past to explain the connection between renewable energy consumption, our primary focus is on renewable energy consumption, as this topic has received insufficient attention in previous research. We use the recently developed methodologies to conduct a more precise analysis of the correlation between the consumption of renewable energy sources. The current study demonstrates the importance of environmental quality when it has serious consequences for human beings. This is demonstrated by the presence of an energy-environment model in the study. As a result, policymakers are concentrating their efforts on implementing policies that will improve the quality of the environment by reducing emissions ( Habiba and Xinbang, 2022 ; Yu et al., 2022 ). It is common knowledge that carbon emissions contribute to climate change; consequently, higher authorities are making some efforts to transition the world toward one powered by renewable energy.

Similarly, the remaining aspects of the research are separated into the following sections: a review of the existing literature (Section 2), followed by data and methodology, results and discussion, and finally, a conclusion and some suggestions for future research (Section 4). (Section 5). (The Fifth Section).

2 Literature Review

2.1 renewable energy and carbon emissions.

It is now common knowledge that if the rate of global warming is not slowed down by implementing effective countermeasures, the entire world will unescapably be forced to deal with ferocious and environmental catastrophes. According to Feng et al. (2022) , the reduction of 25% of world GDP due to uncontrolled greenhouse gas emissions is likely, while limiting emissions will only cost about 1% of GDP. In addition to endangering people’s ability to feed themselves and their families, the continued increase in CO2 emissions, the primary cause of climate change, will cause temperatures to soar even higher. It is important to remember that the world’s energy landscape is dynamic and ever-changing, and this poses a serious problem for countries that rely on imported energy. The global energy landscape is constantly changing and adapting to new developments. This situation has existed in its current form for a considerable period of time ( Shahbaz et al., 2013 ; Khan et al., 2022b ). It is important to note that the areas of conflict in the Middle East contain a disproportionately high concentration of the world’s energy sources. For this reason, in this context, t According to the United States, an economic policy has been developed to promote energy efficiency and lessen America’s heavy reliance on foreign oil. It is possible to attest that RE is an essential component in which investments are being made to guarantee both energy independence and energy security.

On the other hand, the consumption of fossil fuels continues to play an outsized role in the majority of countries’ current economic activities (i.e., Coal, oil, natural gas). However, in recent years, many nations have shifted their focus away from fossil fuels and toward alternative forms of energy generation. This is because When fossil fuels are burned, they greatly contribute to global warming, climate change, acid rain, and other environmental issues. Renewable energy sources, such as hydropower, biomass, wind, Sun, and geothermal energy, would be used in this scenario, have slowly emerged as the pack leader in the global energy advertise. This scenario is based on a scenario in which renewable energy sources have gradually emerged as the leader of the pack. In addition to satisfying the demand for an endless supply of energy, they can also meet t A range of renewable energy sources, especially those that don’t generate greenhouse gases, are needed ( Bhuiyan et al., 2018 ; Mngumi et al., 2022 ).

The most significant contributors to atmospheric carbon dioxide levels are primarily population expansion, industrialization, and Coal and the usage of fossil energy. As a result, there is widespread agreement that renewable energy (RE) must continue its rapid expansion to combat the worrisome rise in average global temperature observed over the past several years. The International Energy Agency suggests using nuclear power, renewable energy, and improvements in energy efficiency as the means via which global temperatures can be reduced to within 2°C of what they were before industrialization. In a study that followed the same pattern as the previous one, Mahalik et al. (2017) conducted research using data acquired from a panel of 17 nations that are members of the Group for Economic Co-operation and Development (OECD) ( Al-Mulali et al., 2015 ). They discovered that the use of RE has, in point of fact, a negative effect on the emergence of CO2 emissions.

Furthermore, ( Handayani and Surachman, 2017 ), researched CO2 emissions have a long-term and short-term impact. According to the results of their study, renewable energy can be regarded as an important factor in reducing carbon dioxide produced during the process of power generation. This was the conclusion of their investigation into the long-term and short-term causality of the CO2 emissions. According to research carried out by Sharma et al. (2021) , renewable energy can serve as a viable replacement for conventional forms of energy derived from fossil fuels and cut down on CO2 emissions significantly. In addition, According to, there appears to be a significant negative impact on CO2 emissions from increased consumption of RE and other forest plantations over a long period of time ( Irfan et al., 2022 ; Wen et al., 2022 ; Xiang et al., 2022 ). A reduction in carbon dioxide emissions will follow after an increase in the proportion of renewable energy in the energy consumption function, which will cause the intensity of renewable energy to rise. It was also believed by ( Li et al., 2021 ) that advancements in technology and a reduction in costs are variables that have made the promotion of RE economically feasible and can enhance carbon reduction. Government therefore can contribute to enhance environmental quality by encouraging the use of RE and by substituting traditional energy sources.

According to a number of other studies, Renewable Energy (RE) does not prevent pollution effectively. While China’s policy to promote renewable energy development has been widely publicized, ( Koengkan et al., 2019 ), it was believed that the decline in co2 emission would be extremely minimal. This is due to China’s focus on renewable energy development. In a survey of 19 countries, ( Irfan Khan et al., 2021 ), found that nuclear energy consumption significantly negatively impacts carbon dioxide emissions. On the other hand, renewable energy consumption has a positive impact. Due to the low share of RE, which has not yet reached a sufficient level to begin reducing CO2 emissions, this situation may have an explanation. There are several ways to look at this situation, including Fossil fuels still make up the majority of the world’s largest and most important industries’ final energy consumption, with Coal accounting for the most. Fossil fuel combustion produces the majority of greenhouse gas emissions, which are then converted into useful forms of energy. Consequently, energy substitution was not achieved despite the limited supply of renewable energy sources. As a result, ( Probst et al., 2021 ), made the point that the sustainability of RE varies greatly across different parts of the world ( Zeng et al., 2017 ). said to be minimal, as well, the negative impact of RE on CO2 emissions Due to rising economic expansion and non-renewable energy usage, the slowing impact may be obscured. Renewable energy (RE) does not significantly negatively impact CO2 emissions. Energy extraction from precious metal deposits, which are necessary for RE development, is also a significant drain on energy resources in its opposite direction. As well as polluting the environment (soil, water, and food), heavy metals like lead and Mercury pose a health risk to humans. The mining sector has also boosted its extraction of precious minerals as a result of the increasing expansion in consumption for RE. Due to the rising importance of Renewable Energy (RE). As a result, mining has wreaked havoc on ecosystems that are otherwise rich in biodiversity.

With the release of carbon neutralisation objectives in a number of nations, many activities have been developed to be extremely efficient with their use and green low-carbon development. This was done in order to ensure that there is the progress made in both areas. As the three countries that produce the most carbon dioxide emissions in the world, India, China, and ISA have all begun competing in a green energy race in an effort to find practical answers to the issue of global warming. In each of these three nations, energy is not only the most significant contributor to overall carbon emissions but also an essential component in laying the groundwork for continued economic and social advancement ( Ye et al., 2022 ). The transformation of energy to green and low in carbon In the wake of this, the two nations are striving to alter the energy policy trend by focusing more on renewable energy (RE). However, because of the expense of decarbonization, it is unclear whether or not RE can serve as a useful tool in pursuing the organization’s objective of reaching carbon neutrality. As a result, we have decided to investigate the possible cause-and-effect relationship between RE and CO2.

2.2 R&D Expenditures and Environmental Degradation

A growing body of economic research emphasizes technological innovation to combat climate change and promote long-term well-being, particularly in the energy sector. In a few of the contributions, the evolution of technological processes was examined. Others look into the most efficient ways to meet the electricity demand, taking into account both financial and environmental considerations, to find the most cost-effective solutions. For Charfeddine and Kahia, (2019) , natural gas is a more important part of Greece’s energy mix and how Community Support Framework Program II has altered energy consumption and CO2 emissions in the country.

The factors that determine innovation and the obstacles to its spread have received a lot of attention. The discussion is centred on two distinct approaches: the first is economical, and the second is technological. Among other examples, imperfect knowledge, R&D spillover effects, and agent-principal problems are all examples of market failures that need to be addressed by government action. According to the methodology, public policy should solve all difficulties, despite of whether market failings are to blame. These challenges include, among others, bounded rationality and uncertainty. The most recent research emphasizes the importance of developing and enacting policies to lower and do away with various kinds of market failures and obstacles. As a result, many scholarly works have been devoted to investigating the myriad of factors that contribute to the failure of numerous industries to innovate.

Several empirical studies have demonstrated the significance of companies establishing environmental goals, as these goals have a direct and indirect impact on the innovation process. This has brought to light the importance of the matter. In this vein, Costa ( Rufei Ma et al., 2021 ) analyzed as part of their innovation initiatives, firms seek to increase energy efficiency. The findings that they obtained for Spanish manufacturing companies highlight the significance of the size of an enterprise concerning innovation in energy efficiency. In addition, they discovered that businesses whose innovations are geared toward lessening their impact on the environment are more likely to innovate in ways that lead to greater energy efficiency.

There are other studies that look at how energy intensity changes across countries or regions and examine if it converges with time. According to these studies, energy efficiency improvements have been blamed for the reduction in energy intensity between countries and regions. ( Meijer et al., 2019 ) say that changes in the overall energy intensity of the economy and changes in the energy intensity of specific economic sectors are the two most important factors that determine the progression of energy intensity. ( Mehmood, 2021 ; Zafar et al., 2021 ) argue that innovative effort is critical to reducing energy intensity in the business context. In general, Porter and Van der Linde had already considered this possibility. There is a connection between the amount of money spent by the government and the structure of energy consumption that is discussed in ( Samour et al., 2022 ). Efforts to empirically link greenhouse gas emissions with economic agents’ innovative efforts, measured by R&D, have recently taken a special interest in the topic. Consequently ( Rauf et al., 2018 ), investigated the link between the amount of public funding allocated to energy research and the energy intensity levels recorded in the EU-15 countries between 1974 and 2007. He performed a Granger causality analysis, in which he specified an error correction for the model in order to accomplish this. According to these findings, energy consumption and government-funded research and development are linked in the long term, and public funding for energy research is linked to reduced consumption in the short term.

Qaiser (2022) examined the relationship between public energy R&D and CO2 emissions per unit of GDP and its two components: energy intensity and carbonization factor, using panel data for 13 developed economies from 1980 to 2004. From 1980 to 2004, researchers worked on this project. The findings show that increasing public investment in R&D is not enough to boost the innovation process on its own. Government funding for energy-related research has successfully increased energy efficiency, but it has had no impact on the carbonization factor or emission intensity ( Martí-Ballester, 2021 ) used the Environmental Kuznets Curve model to show that public spending on policies for R&D and I energy has an inverse relationship with greenhouse gas emissions in Spain. In their research, they presented this evidence.

Oil and gas had a positive income elasticity, while Coal’s demand was negatively elastic. According to this, cleaner energy will be encouraged if economic growth is maintained. Researchers have discovered that the advancements in fossil fuel R&D are driven by the consumption of fossil fuels, which in turn drives consumption of fossil fuels through the dynamic links between energy use and energy R&D. Global greenhouse gas emissions (GHG) emissions have been rising since 1992, according to ( Jingjing Chen et al., 2021 ). The latter studied 24 OECD countries from 1992 to 2010. They show that reducing greenhouse gas emissions can be linked to government efforts in innovation and energy substitution. An integrated assessment model with multiple externalities and an endogenous representation of energy-related technological advancements were used by ( Tao Zhang et al., 2022 ) in their study. A variety of innovation policies were examined, both on their own and in conjunction with various other mitigation policies. According to the findings, innovation policies alone will not be enough to stabilize global emissions. Combining climate and innovation policies yields gains of 10% for a strict climate policy and 30% for a more moderate one. Climate and innovation policies can have a positive impact if they are combined. However, these gains are diminished when more plausible global innovation policy arrangements are considered. According to a study by ( Xiong et al., 2020 ) using a sample of Japanese manufacturing companies between 2001 and 2010, green research and development positively correlated with a financial performance at the firm level. The researchers discovered a link between green R&D and increased carbon emissions.

2.3 Financial Sector and Carbon Emissions

Numerous studies have focused their attention on determining whether or not there is a correlation between economic growth and CO2 emissions. Empirical analyses of data from Pakistan (Kuwait, and Malaysia, lower-middle-income countries, upper-middle-income countries, and high-income countries, South Asian economies (On the other hand, some case studies have found the complete opposite to be true. For instance ( Xiong et al., 2020 ), looked at 46 countries in sub-Saharan Africa between 2000 and 2015 and discovered that financial development tended to increase CO2 emissions. The research conducted by Acheampong utilized the system-generalized method of moments approach, which can perform a more accurate correlation analysis by addressing the endogeneity problem. In the same vein, a case study carried out in Turkey revealed that financial development had a significant impact on CO2 emissions. This impact was ranked second only to economic growth and urbanization in terms of its importance. A very similar situation to this one was discovered to exist in 24 of the countries that make up the Middle East and North Africa (MENA) region. In these countries, it was discovered that financial indicators (the proportion of domestic credit extended to the private sector as a % age of real GDP) did not have any connection to reducing carbon emissions.

In addition, some prerequisites need to be met before there can be a significant connection made between economic growth and CO2 emissions. As per capita income and other output indicators have a greater impact on carbon mitigation, policies are urgently needed to strengthen financial development in these economies. Financial variables enter into the equation for reducing emissions only when the financial sector is well-developed and liberalized. Medium-sized economies like Pakistan face this challenge. In periods of liberalization, financial variables play a role in emission mitigation. Because there are many ways to define financial development, different financial indicators each have their unique impact on the amount of carbon dioxide released into the atmosphere. Financial development and the market value of listed companies are important factors in reducing CO2 emissions. At the same time, the efficiency and volume of stock trading are important factors that prevent carbon mitigation in China’s provinces, according to a panel data analysis. Similar findings were found in another study investigating the effect that financial development has on the CO2 emissions embedded in trade. This study found that increasing financial scale leads to an increase in emissions, whereas increasing financial efficiency and financialization leads to a decrease in the emissions embedded in traded goods and services ( Quan et al., 2021 ).

The financial sector’s variables have been linked to the CO2 emissions determinants. Financial development has a negligible impact on CO2 emissions when compared to the use of renewable energy sources. For example, according to ( Wu et al., 2022 ), renewable energy can effectively reduce CO2 emissions in the MENA region, whereas the development of financial markets had no significant effect on emissions reduction. The researchers also discovered a link between renewable energy use and financial factors ( Xu et al., 2021 ). Using less renewable energy as a result of financial development in MENA countries may lead to a decline in environmental quality. This suggests that the Middle East and North Africa (MENA) region needs to better allocate resources to promote positive financial outcomes.

Further evidence has been uncovered that financial development is critical in reducing carbon emissions and energy consumption to a more manageable level. Climate change can be traced back to both of these factors. When it comes to carbon emissions, for example, research has shown that economic development can increase carbon intensity in certain Chinese provinces while at the same time significantly lowering carbon intensity in neighbouring provinces. The net result is lower carbon dioxide emissions (CO2) per unit of GDP. An opposing view was put forth by ( Ji et al., 2020 ), who asserted that the financial scale in China is positively associated with carbon intensity and thus promotes increased carbon emissions. The authors concluded that the country’s efforts to reduce carbon emissions should be bolstered in this area. This favourable result was found to be false in the short term. However, in the long run, it was found to be correct. Existing research has used various financial variable definitions and data structures and methods that differ. This may be one of the reasons for the disparity in the findings of the various studies. However, the ratio of bank deposits to GDP used by ( Ji et al., 2021b ) and ( Babu et al., 2020 ) to measure financial development differed. The following table includes these two ratios. The Autoregressive Distributed Lag Approach–Error Correction Model (ARDL–ECM) was used with time-series data by ( Xi Chen et al., 2021 ), while ( Muhammad et al., 2017 ) built a panel dataset with spatial econometrics methodology and used ARDL–ECM. This article cites both sets of researchers.

3 Data and Methods

3.1 model construction.

It is necessary to conduct a quantitative analysis of the impact that green finance has in order to systematically investigate the impact that green finance has on CO2 emissions in China. Investments made by the private and public sectors in projects aimed at reducing energy consumption and protecting the environment are known as green investments. The public sector is the primary focus of this article; therefore, the definition of green investment will be based on the proportion of China’s total fiscal expenditure allocated to programmes that encourage energy efficiency and environmental stewardship. This index was chosen for use for a couple of different reasons. As a starting point, there is a lack of reliable data on the private sector’s capital equity investment in energy conservation and environmental preservation. It should be noted that the data presented here is not complete ( Lv et al., 2021 ). A second thing to keep in mind when looking at green securities and green credit financing is that they mostly apply to the private sector and thus show how private sector investment affects green finance. As a result, it is thought that excluding private sector equity investments from the green investment index will better represent the impact of government spending on the index.

Clean energy sources (such as solar and wind power) and natural gas infrastructure construction have benefited from China’s rapid economic growth. An infrastructure for natural gas can now be built. Higher incomes can also help increase the availability of affordable clean energy and promote modernization and carbon reduction in household energy consumption (reference here). China’s west-east transmission projects for both electric power and gas have contributed significantly to the rational allocation of resources and optimization of the country’s energy structure ( Raberto et al., 2019 ). In addition, policies that reduce emissions (such as Coal to electricity and Coal to gas) cannot be ignored because of their positive effects. Coal to electricity and Coal to gas are two examples of these policies.

3.2 Econometric Model

Following the investigation conducted in Section 3, this study conducts additional research into China’s Green finance’s effect on CO2 emissions. Based on this, CO2 emissions are the dependent variable, and green finance, along with renewable energy, economic growth, openness, R&D expenditures, and urbanization, is the core independent variable. This study uses time-series data for empirical analysis, and it takes into consideration the possibility of a lag effect caused by CO2 emissions ( Jingxiao Zhang et al., 2021 ). The econometric model is constructed as follows:

By taking natural log on both sides,

In contrast, the variables LC, LGF, LRE, LG, LOP, LRD, and LUR in Eq. 2 represent the natural log of carbon emissions, green finance, renewable energy consumption, economic growth, output, and research and development expenditures, and urbanization, respectively. Similarly, t represents the time period between the years 1998 and 2018. This study uses balanced time series data for the empirical analysis to investigate the impact of green finance on CO2 emissions in China in a quantitative manner. In addition, the sample data for the variables in this study are processed using the logarithmic method in order to eliminate the possibility of heteroscedasticity and prevent data fluctuations. The data of China’s CO2 emissions (abbreviated as C) serve as the primary dependent variable in this study. These data were obtained from the World Bank.

In addition ( Zaman et al., 2016 ), the level of economic growth (denoted as G) is measured by gross domestic product (GDP) in US current dollars, renewable energy consumption (denoted as RE) is measured by the % age of total energy consumption, openness (denoted as OP) is measured by the ratio of exports plus imports to GDP in US current dollars, and the urbanization (denoted as UR) is measured by the proportion of the total population, green finance (denoted with GF), and research and development (RD). The World Development Indicator is the source of information used to determine the levels of GDP, RE, OP, R&D, and UR. Table 1 contains the definitions of the variables mentioned above and their descriptive statistics, which include observations, standard deviation, mean value, minimum value and maximum value.

TABLE 1 . Descriptive statistics of variables.

3.3 Estimation Procedure

3.3.1 unit root tests.

The ADF test developed by ( Shan et al., 2021 ) and the Zivot–Andrews test with a single structural break is utilized in this study. In order to determine the extent of the datasets’ integration, the ADF test is carried out. One of the most significant flaws of this method is the possibility of misinterpreting potential violations of procedure within a collection of facts that did not originate in a fixed location. Alternately, if the analyzed sequence contains a structural split, it can’t support the null hypothesis of unit origin. This problem is dealt with by employing the Zivot–Andrews root test, which involves making one structural break.

3.3.2 Bayer and Hanck Cointegration Test

A variety of cointegration methods have been documented in econometric literature to determine whether the variables are co-integrated. Researchers such as ( Wu et al., 2021 ) have found that if there is some kind of linear stationary arrangement between several series, the long-term association between the series is more likely to hold (2006). Co-integration and non-cointegration null hypotheses differed less when results from each of the tests were taken into account. In a similar vein, it is possible to get more accurate results by calculating the statistical significance of each test (M. S. Mubarik M. S. et al., 2021 ). As previously mentioned, this investigation used the Bayer and Hanck cointegration method ( Eqs 3 , 4 ).

Likewise, Prob. EG, Prob.JOH, Prob. BO, and Prob.BDM is denoted with individual probabilities of each test.

3.3.3 FMOLS

Cointegration regression estimates can be produced using this unique quantification method ( Du et al., 2019 ). Without compromising the robustness of the variables, the method addresses the problem of endogeneity and autocorrelation. In addition to this, it makes the existence of the endogeneity problem and serial correlation possible ( Eq. 5 ).

In addition, it makes it possible to co-integrate Yi and Xi with the slope of i where i may or may not be homogenous across i. Because of this, the equation can be written as Eq. 6 , which is its shorthand form.

Similarly, t = 1, 2, ….., T and i = 1, … …., N. The simultaneous covariance is showed as Ω01, while the weighted sum of auto-covariance is Γi. Thus, FMOLS equation can be written as Eq. 7 ,

3.3.4 MS-ECM Approach

EKC’s hypothesis was tested using the MS-ECM and FMOLS techniques, respectively. Cointegration estimation can benefit greatly from this methodology. This model’s universal form can be written as ( Eq. 7 ).

The rate of adjustment to long-term equilibrium is measured by β; independent variables Xt and the explained variable Yt are included in the equations. And the first difference operator is Δ. Comparing the MS-ECM to other error correction models, there are numerous advantages. Without prior knowledge of the number of regimes, the number of breaks, and the date of breaks, this methodology does not provide any preliminary information on these variables.

3.4 Error Correction Based Granger Causality Analysis

Given that the two econometric methods used in this study, namely FMOLS and MS-ECM, cannot trace the causality association among the variables, we move on to the Granger causality model. In the event that there is no evidence presented for the cointegration of the variables, the Granger causality test will be defined using the vector autoregression test in the first differences ( Luo et al., 2019 ). If we find evidence of cointegration, however, we will have to include a one-period lag error correction term in the Granger causal test mode (ECT-1). Suppose the findings show a long-term connection between the indicators of environmental quality that were specified. In that case, the next step is to quantify the vector error correction model (VECM), which is given in the Eqs 10 – 15 that are presented in the following paragraph by succeeding Engle and Granger (1987) .

When a system is subjected to a shock (S), ECT-1 is used to correct long-term Granger causality errors in order to restore equilibrium.

4 Results and Discussion

The findings of the correlation matrix are detailed in Table 2 . At the 1% significance level, there is a positive association between green finance and carbon emission. At a significance level of 1%, this table also reveals that the LRE has a negative association with CO2 levels, while economic growth and the LRE have a positive association with emission levels. At the same time, openness and urbanization have a negative relationship with CO2 emissions, which is significant at the 1% level.

TABLE 2 . Correlation matrix.

The results of the Bayer-Hanck test of cointegration, a combined cointegration test, are shown in Table 3 below. These results validate the presence of a cointegration association among the variables that were chosen.

TABLE 3 . Bayer-Hanck cointegration test.

At the 5% level of significance, the critical values are significant. The results of three different unit root tests are displayed in Table 4 . These tests include ADF and ZA. According to the findings, not one of the variables is significantly stationary, but all of these variables reach a point where they are significantly stationary after the first difference. On the other hand, the ZA test takes into account structural breaks while conducting unit root analysis. ADF does not take this into account. Selected macroeconomic and environmental variables are non-stationary when they are level, but they become stationary when the first difference is between them. This harmony between the results of these three tests is confirmed because the variables become stationary when there is the first difference ( Yan et al., 2020 ). The trace and maximum Eigenvalue tests are the most likely approach to run the cointegration test because there is no mixed stationarity of level and first difference, and the sample size is quite large. For example, the assumption that there are no mixed stationarities of level and first difference is based on these tests.

TABLE 4 . Outcomes of ADF and Zivote unit root test.

The results presented in Table 5 above indicate that there are LR relations between energy intensity, carbon emission, and the exogenous variables that pertain to it. All of the given H0 further elaborate that there is no cointegration. This equation can be rejected if the trace and maximum Eigenvalues are higher than the critical value with a significance level of 5%. ( Hardin-Ramanan et al., 2018 ). The model in question contains a total of four cointegration equations.

TABLE 5 . Outcomes of Trace and Eigen Values tests.

4.1 Long-Run Outcomes of FMOLS and MSR Estimators

This study uses the Fully Modified Ordinary Least Square (FMOLS) and Markov Switching Regressions (MS) model for reliable outcomes concerning environmental quality. FMOLS and MS are both models for Markov switching regressions. The primary objective of this research is to calculate the zero carbon in the China economy. As a result, the current study emphasises green finance, green energy, and R&D expenditures as the primary factors that can influence the level of emissions. On the other hand, economic growth, openness, and urbanization will serve as control variables. According to the co-efficient that has been provided for green finance, it has been shown to have a negative association with carbon emissions. According to the specifications of the FMOLS and MS models, this implies that a one % increase in this factor would result in a decrease in carbon emissions of 0.895% and 0.541%, respectively. Increasing the amount of green finance leads to better financial efficiency and more innovative green financial products and services, forming a positive feedback loop with the environment and promoting society’s green development. This is why green projects and green technology are beneficial to the growth of green finance. This turn helps to foster a society that is more environmentally conscious. However, the impact of green finance on reducing carbon emissions is somewhat impressive; however, the marginal effect tends to decrease as the economic level improves. In the context of the industrial structure transformation, the pattern of change is becoming more complicated, and the rate of emission reduction is accelerating. This suggests that green finance has a more pronounced impact on reducing carbon emissions at this stage of economic development. The high growth potential of intangible assets and environmentally friendly businesses can be achieved not only by the high value of the product or technology but also by the results of market transformation, resulting in green technology innovation with increased carbon reduction and a stronger financial system in terms of greenness, prosperity, and development. The key to receiving financial support is a large number of green projects and green technology. On the other hand, as the economy continues to advance, new factors that influence the reduction of carbon emissions are beginning to emerge, or some previously unconsidered factors are gradually becoming relevant. Therefore, in situations where there is a low proportion of the secondary industry, green finance has a greater impact on reducing carbon emissions.

In a similar vein, using renewable energy sources has a negative impact on the amount of carbon dioxide released into the atmosphere. This explains why a 1% significant increase in renewable energy consumption would cause a decline in emissions levels by 0.762% under one specification and 0.814% under the other specification. Renewable energy use has been shown to contribute to economic growth while reducing environmental damage in 85 developed and developing economies studied between 1991 and 2012 by ( Ali Shah et al., 2021 ). However, findings from ( d’Amore-Domenech et al., 2020 ) in 19 developed and developing economies show the opposite, showing that renewable energy use does not contribute to economic growth and reduces environmental damage. The results for the selected country are consistent with the findings of Bhattacharya. Therefore, it is possible to say that switching from the use of energy sources that do not regenerate to the consumption of energy sources that do regenerate (which is synonymous with a decline in the level of dependency on fossil fuels) contributes to an improvement in the quality of the environment ( Xie et al., 2019 ). In addition, the elasticity estimates highlight that the CO2 emission figures of the selected economy are relatively inelastic to positive shocks to the consumption levels of renewable energy.

In a similar vein, economic growth also performs admirably to improve the general standard of an economy, but it has a detrimental impact on the level of emissions. Therefore, the estimated results demonstrate a positive relationship between economic growth and carbon emissions. They imply that an increase in this factor by 1% would cause an increase in carbon emissions of 0.638% and 0.593%, respectively. The estimated results show that economic growth is positively correlated with carbon emissions. As a result, the damage done to the environment gets worse as countries’ economic growth tends to increase. This relationship is only observed when no technologies exist to improve energy efficiency, energy savings, and renewable energy, all of which are inaccessible to the economy in question due to the high cost of these technologies. Because a higher level of economic growth involves a wider range of economic activities, a higher level of energy consumption goes hand in hand with that level of growth ( Fujii and Managi, 2019 ). As a direct result of this, the byproducts of energy consumption emit pollution into the atmosphere, which lowers the quality of the environment.

Trade openness is one factor that is considered a determinant of carbon emissions, and it is known to have a detrimental effect on carbon emissions. According to the estimated coefficient, a one % increase in this factor would decrease carbon emissions by 0.235% and 0.452%, respectively. In the long run, increased commercial openness is associated with increased CO2 emissions. As a result, the propensity for increased trade openness to lower emissions is likely to materialize more in the economy that has been specified. Furthermore ( Luo et al., 2019 ), discovered that reducing carbon dioxide emissions in the United States is only achieved in the long run when trade openness is increased. In the long run, increased trade openness has been shown to have a negative impact on carbon dioxide emissions in the United States. This was confirmed by ( Gao et al., 2021 ). There is a correlation between increased trade openness and lower emissions, and this correlation can be explained. Countries initially exposed to greater trade openness might not have the technological capacity to compete favourably on the international market. As a result, there is a good chance that the product will generate a lot of pollution. On the other hand, throughout a longer period of time, the technical capacity of the economy may grow as a result of opportunities presented by the influx of foreign capital, local investments in technology, and exposure to improved international management practices. Consequently, over time, there will likely be a reduction in the amount of pollution caused by the production.

In the final section, urbanization is also considered a factor in environmental pollution. According to FMOLS and MS regression parameters, an increase of this factor by one % would lead to an increase in the overall level of pollution in the environment of 0.698% and 0.829%, respectively. The migration of people from rural areas to newly emerging cities is one of the earliest signs of urbanization. This migration is responsible for the concentration of manufacturing activities and the development of the industry. To begin with, the labour force working in agricultural fields has been decreasing due to urbanization, which has led to the expansion of the two-sector economy. In this stage of industrial development, a significant amount of fossil fuels, such as coal and gasoline, are utilized for production activities due to the prevalence of primary technologies. Compared to agricultural production with low energy intensity, a significant amount of carbon dioxide is released into the atmosphere. Second, the requirements and patterns of behaviour of private households change as a result of urbanization. Families in urban areas are more likely to rely on goods and services provided by businesses (M. Mubarik et al., 2021a ). The ongoing expansion of energy consumption, such as heating installations and electric appliances, is a significant contributor to the production of greenhouse gases (GHG). Third, urbanization drives up the demand for transportation on both the intercity and intracity levels of the city ( Dongyang Zhang et al., 2021 ). The growth of both private and public transportation will inevitably degrade the quality of the environment. One more thing to consider is the increased need for urban infrastructure brought on by increased industrialization and urbanization in the cities. The building of public infrastructure, including highways, bridges, sewage systems, and other similar projects, requires significant energy and results in increased carbon emissions. In addition, as urbanization continues, there is a steady decline in the amount of land that is readily available. It is more common to construct multi-story buildings, which are more expensive and consume more power. On the other hand, the limits of cities will become increasingly expansive. There is a risk that agricultural land and natural areas will be extensively appropriated. All of these mechanisms work together to drive up carbon emissions.

In a similar vein, the results of research and development expenditures have been in line with what was anticipated initially. Because of this, it has a bad reputation in relation to carbon emissions. According to the information provided by this association, a one % increase in this factor would result in a decrease in carbon emissions in china of 0.852% and 0.735%, respectively. It is possible to explain it using appropriate logic and appropriate prior studies. The findings regarding the effect of the money spent on research and development are more encouraging as they indicate a negative impact on emissions. The evidence currently available, such as the studies conducted by ( Dong et al., 2022 ) on BRIC countries, is consistent with the finding that R&D has a negative impact on emissions. On the other hand, ( Gouda et al., 2021 ), found that the level of R&D activity has an uncertain impact on the amount of carbon emissions produced. To achieve the goal of having net zero emissions by the year 2050, however, economies will need to increase their level of investment in low-carbon innovations, as recommended by the Committee on Climate Change (CCC). The findings of our empirical research lend credence to this proposition. The adverse effects of the financial crisis on carbon emissions are prima facie evidence of the changes in the quantity of aggregate demand and its composition that have occurred as a direct result of the financial crisis.

4.2 Model Stability Tests

This study performs the SERIAL, ARCH, WHITE and RAMSAY tests to check out the model stability. According to the given p -values, there is no serial correlation in the selected model.

4.3 CUSUM and RS-CUSUM Tests

After determining the requirements for the definition of each contingent’s residual stability, CUSUM and RS-CUSUM are subjected to testing. Figure 1 number provides definitions of the results for mean stability and variance (CUSUM and RS-CUSUM).

FIGURE 1 . CUSUM and RS-CUSUM tests results.

4.4 Granger Causality Test

The results of the Granger causality test are displayed in the given Table 8 , which can be found here. It emphasizes the uni-directional and the bi-directional causality associations that exist among the variables. For example, because researchers discovered a bi-directional causality association between openness and carbon emissions, any change in openness would lead to an increase in carbon emissions and vice versa. In addition to this, it was discovered that there is a two-way causal association between the consumption of renewable energy and the openness and green finance to R&D expenditures under the chosen model ( Ganda, 2019 ). On the other hand, there was a one-way association between energy poverty and openness and urbanization. In a similar vein, emissions contribute to urbanization, and economic growth is a contributor to both energy poverty and the consumption of renewable energy sources. During the most recent cycle, the green finance granger causes R&D expenditures, while the urbanization granger causes renewable energy consumption and overall economic growth ( Tables 6 , 7 , 8 ).

TABLE 6 . Outcomes of FMOLS amd MS estimators.

TABLE 7 . Stabiity tests.

TABLE 8 . Outcomes of Granger causality test.

5 Conclusion and Policy Recommendations

The topic of major concern for people worldwide right now is the pollution of the natural environment. Consequently, the countries of the world are working hard to develop and put into effect credible policies that will assist in achieving economic growth without negatively impacting the environment. Emerging economies, in particular, are expected to make significant contributions to global output and, as a result, are also likely to account for a significant portion of the global greenhouse gas emissions. For this reason, it is of the utmost importance that environmental pollution is brought under control. In China, conduct a comprehensive investigation into the dynamic linkages between green finance and CO2 emissions. In addition, the consumption of renewable energy, expenditures on research and development, economic progress, openness, and urbanization are taken into consideration as the variables that explain carbon emissions in this study ( Xie et al., 2018 ). The primary findings are that green finance in China has shown a significant downward trend in carbon emissions in China. To put it another way, an increase in the quantity and quality of the environment would result from a significant improvement in green finance. However, there is a negative relationship between the expenditures on renewable energy, openness, and R&D, as well as the emissions of carbon dioxide. Urbanization and economic growth have been shown to have a positive relationship with carbon emissions, as was discovered last. In addition, the Granger causality test is carried out in this investigation so that an estimate of the causal association between variables can be obtained.

This study suggests some policy changes that could be made in order to achieve zero carbon emissions. Based on our findings, we have concluded a significant connection between economic growth and environmental degradation. When applied to a policy context, this means that in situations where the financial sector is important for the real economy, it also has crucial implications for the stability of the environment. As a result ( Wang et al., 2019 ), economic development and financial activities ought to be centred on enhancing the quality of the environment. The promotion of forms of financialization that are more environmentally friendly ought to be a priority. Public and macroeconomic policy development should centre on environmentally friendly and sustainable forms of finance. It should be discouraged to allocate financial resources to industries that have a lower environmental impact efficiency. At the same time, resources should be allocated to industries that positively impact the environment. Based on our empirical research findings on R&D expenditures, we have concluded that R&D expenditures are good for the natural environment. Because the statistics demonstrated very strong and empirically robust results, we have strong confidence in our ability to infer that research and development are essential to addressing environmental challenges. Because of this, it is necessary to direct both public policy and the distribution of resources toward research and development when formulating policies and, in particular, when attempting to reduce carbon emissions. This will make it easier for us to accomplish our goal of reducing carbon emissions to a net-zero.

The development of the economy, the development of the environment ( Wang et al., 2021 ), and the use of energy sources within the country should all be considered when formulating energy policy from the perspective of policymaking. In addition, the encouragement of the use of renewable sources of energy carries with it benefits not only for the state of the environment but also for the nation’s economy. Therefore, economic growth is necessary if sufficient resources are to be generated for the research and development of technologies based on renewable energy sources and the infrastructure associated with these technologies. In a similar vein, policymakers need to put in place appropriate incentive mechanisms in order to foster the growth of renewable energy sources and increase their availability to markets. The formation of partnerships between the public and private sectors makes it easier to transfer technology to bring renewable energy projects to market. Our research lends credence to the concept that renewable sources of energy have a wide range of positive effects, including the reduction of carbon emissions, improvement of air quality, and acceleration of economic expansion.

As a result, China ought to thoughtfully consider appropriate trade policies and the utilization of renewable energy, alternative energy, and nuclear energy to minimize the adverse effects on the environment. In addition, it would appear that liberalizing trade would be beneficial to the environment for the economy in question. As a result of this, the promotion of environmentally friendly and efficient products in their use of energy is encouraged in these countries. Second, even though it has been determined that globalization does not contribute to environmental prosperity in India, it is of the utmost importance to make certain that the use of renewable energy resources meets the increase in energy demand that is a direct result of globalization. In this regard, a particular economy may consider trading renewable energy with the countries that are immediately adjacent to it. As a result ( Xia et al., 2019 ), the environmental outcomes associated with the globalization of trade may be improved further.

Policymakers in China should prioritize accelerating the process of sustainable urbanization and improving public infrastructure to encourage the rapid development of urban cities and take advantage of the benefits of agglomeration effects and economies of scale. This will have a positive impact on India’s economy, which will have a positive impact on urbanization. Typically, this stage is accompanied by the development of the industry and structural shifts in both the economy and society. In addition, governments should develop a guide to sustainable lifestyles, including topics such as environmentally friendly travel and the utilization of renewable energy sources. Countries that have experienced rapid urbanization should prioritize efficient and well-organized urban expansion to reduce the likelihood of both traffic congestion and overcrowding. This study could be expanded in the future by looking at policy thresholds and critical mass levels at which renewable energy could boost economic growth without negatively impacting environmental quality.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: FDI.

Author Contributions

According to the energy-environment model, there is a link between RE and CO2 that may be seen empirically. It has been shown that emissions of carbon dioxide have a detrimental effect on RE. This demonstrates how the tough environment is giving more importance to pollution levels as it becomes more difficult. Policymakers are encouraged to implement policies that encourage a low-carbon economy as a result of the rise in carbon dioxide emissions. RE has also been shown to be heavily influenced by state-by-state policy differences in the USA. CO2 emissions contribute to global warming, and the government should do something to make it easier for people to move to a renewable energy (RE)-based world.

Supported by the Philosophy and Social Sciences Foundation of Ministry of Education of China (Grant No. 13JHQ052), the Excellent Postgraduate Courses Fundation of Beijing University of Technology (Grant No. CR20201720).

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: zero emission, global warming, renewable energy, SE-SBM, CO2 emission

Citation: Feng H (2022) The Impact of Renewable Energy on Carbon Neutrality for the Sustainable Environment: Role of Green Finance and Technology Innovations. Front. Environ. Sci. 10:924857. doi: 10.3389/fenvs.2022.924857

Received: 20 April 2022; Accepted: 16 May 2022; Published: 07 July 2022.

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Copyright © 2022 Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Haiyan Feng, [email protected]

This article is part of the Research Topic

Technology-Enabled Circular Economy Practices in Energy Production and Consumption Patterns

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Green Energy Pathways Towards Carbon Neutrality

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• Published: 28 March 2024

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• George E. Halkos   ORCID: orcid.org/0000-0002-2772-5306 1 &
• Panagiotis-Stavros C. Aslanidis   ORCID: orcid.org/0000-0002-3980-0633 1  

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Trying to reach carbon neutrality is by no means plain sailing in times of energy crisis, price volatility, and war. The European Green Deal (EGD) prioritizes green pathways, but it is not enough when it copes with greenhouse gases (GHGs). The present research utilizes the Malmquist–Luenberger productivity index (MLPI) to estimate advancements in total factor productivity (TFP) in the European Union (EU). The study uses panel data from 1995 to 2019, in addition, there is comparison between two periods: 1995 – 1996 and 2018 – 2019, would provide important information about TFP progress or recession during a turbulent European era. Two MLPI models are applied, one that utilizes only non-renewable energy sources (NRES), while the other adopts renewable energy sources (RES). Encompassing inputs such as: electricity generation, labour force, and gross fixed capital formation (GFCF); desirable output: gross domestic product; and undesirable outputs: carbon dioxide (CO 2 ) and methane (CH 4 ). There is average productivity progress, more specifically the MLPI average productivity for NRES and RES is 2.14% and 7.34% respectively, meaning that the RES adoption leads to greater productivity performance by almost three times. This novel analysis might offer useful and practical information to policymakers through the measuring of TFP in order to effectively attain and accomplish carbon neutrality objectives.

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1 Introduction

The path towards carbon neutrality in the European Union (EU) and the United Kingdom (UK) is a subject of great concern. Nevertheless, keeping in pace with the European Green Deal (EGD) is by no means plain sailing for many EU members, specifically in a tumultuous era of full of enigmas: energy crisis, inflation, climate change, and war. Typically, copying with climate change without trying to achieve carbon neutrality is a bit of oxymoron, hence it is understandable that this transition is crucial for building a sustainable future.

Moreover, energy poverty levels are rising due to inflating prices in energy, making unsustainable the modern way of life (González-Eguino 2015 ; Halkos and Gkampoura 2021 ; Halkos and Aslanidis 2023a ). Greenhouse gases (GHGs) pose also robust strains on climate change mitigation and adaptation, especially the CO 2 and CH 4 emissions are multiplying based on a plethora of factors, such as the rampant consumption and production patterns and the ‘’unsustainable’’ economic growth (Barbier 2011 ; Cock 2014 ; Nyborg et al. 2016 ; Vertakova and Plotnikov 2017 ; Acheampong 2018 ; Karydas and Xepapadeas 2019 ; Menuet et al. 2020 ; Xepapadeas 2021 ; Koundouri et al. 2021 ; Cai et al. 2022 ; Chatzistamoulou and Koundouri 2022 ; Fotopoulou et al. 2022 ). Additionally, energy demand can be as well a subject of different forcing factors, such as population growth and economic activity (Timilsina 2020 ). Hence, considering all of the above, how can carbon neutrality be a tangible future and not a utopian one?

Green economy could strike a balance between all the previous enigmas by promoting carbon neutrality and enabling universal access to energy. Green economy exploits different types of energy sources, either renewable energy sources (RES), or non-renewable energy source (NRES). The present study would examine solar, wind, and hydroelectric energy from the RES perspective (Denholm et al. 2010 ; Ellabban et al. 2014 ; Mac Kinnon et al. 2018 ; Halkos and Gkampoura 2020 ), whereas NRES are composed by coal, oil, natural gas, and nuclear energy. Moreover, the natural gas (NG) and specifically the liquified natural gas (LNG) would be also discussed as important intermediates in the green transition era (Elgohary et al. 2014 ; Thomson et al. 2015 ; Mac Kinnon et al. 2018 ; Lindstad and Rialland 2020 ; Song et al. 2020 ).

European Commission has paved the way in delivering proper institutional framework on green energy transition, to exemplify: the EU solar energy strategy; strategy on heating and cooling; wind energy developments and EU nature legislation; offshore renewable energy; and especially the REPowerEU plan for copying with the Russian invasion in Ukraine and its implication in NG supply (EC 2016 , 2022 ; E.C. 2020a , b , 2022 ; D.N.V. 2022 ; I.E.A. 2022 ; Halkos and Aslanidis 2023b ). Though how can electricity generation be measured for pathfinding a proper green transition without emitting GHGs and by going towards sustainable development?

A proposed methodology is by estimating the total factor productivity (TFP). TFP might be a happy medium in bridging electricity generation with low GHGs emissions, measuring it ultimately the clean energy generation performance. Furthermore, TFP is a factor of great attention when having to scale up energy supply chains in order to achieve affordable, secure, and sustainable energy development (I.E.A. 2022 ; IRENA 2022 ).

The present study would (i) emphasize the main advances in RES and NRES in order to achieve carbon neutrality; (ii) employ linear-programming methodology to cope with undesirable outputs; (iii) delve into TFP and its intrinsic parts; and (iv) compare two MLPIs with NRES and RES to distinguish possible interrelations on halving GHG emissions. The structure of the study consists of the following sections: 1.1. would accent the importance of NRES (i.e., coal, oil, NG and nuclear) as traditional energy sources and the significance of RES (i.e., solar, wind, and hydro) as green energy solutions concerning the final stage of green transformation; while Sect.  1.2 . contains the main advancements on data envelopment analysis (DEA) and the theory of MLPI. Data and methodology are presented on Sect.  2 ; whereas the main results and discussion aspects are expanded on Sects.  3 and 4 respectively; at the end, on Sect.  5 the concluding remarks and central policy implications are being vindicated.

1.1 Answering Global Energy Conundrums: Carbon Neutrality

Energy demand is advancing in parallel with economic growth and peoples’ welfare (Timilsina 2020 ), accordingly, energy resources can be categorized into ‘’primary’’ (e.g., solar or wind) and ‘’secondary’’ (e.g., NG). The former can directly produce energy services, whereas the latter need an ‘’energy transformation’’ for delivering secondary energy commodities. Nevertheless, rebound – direct or indirect – effects are looming, representing the discrepancies between rising energy efficiency and not an expected diminishing energy consumption. The main reason of rebound effects is the augmented energy use due to cheaper complementary energy commodities (Gillingham et al. 2016 ; Bruns et al. 2021 ; Saunders et al. 2021 ).

GHGs play also a significant role in energy transition and sustainable development, consequently regulatory authorities try to pose restrictions on GHG emission to combat climate change. The EU is on track to green transformation through on the EGD framework which aims the halving (based on 1999 levels) and zeroing of GHGs by 2030 and by 2050 respectively (E.C. 2019 ; IRENA 2022 ). In parallel, the International Maritime Organization I.M.O. ( 2018 ) has a bit similar pathway, as it has put the target at halving GHGs by 2050 (based on 2008 levels). However, it is the challenge of reducing oil consumption that dampers green transition in transport sector (E.E.A. 2020 ). Therefore, the main achievements in RES and NRES technologies are going to be analyzed.

RES can significantly contribute to green transition. Though, variability and uncertainty are intrinsic parts of the photovoltaics (PVs) and wind turbines, this is also one reason why they are being called as ‘’ intermittent ’’. Thus, there is no stable electricity production due to external factors, hence, the necessity of energy storage is of utmost importance nowadays (Denholm et al. 2010 ).

Solar energy can be categorized into three general infrastructures: PVs, concentrating solar power (CSP), and solar heating. Firstly, PVs can be characterized by high efficiency even with low levels of sunlight, pivotal for impoverished communities and developing countries (Ellabban et al. 2014 ). The rapid conversion of sunlight into electricity can be described also as a great potential for constructing sustainable regimes (Halkos and Gkampoura 2020 ). However, PVs have among the higher emissions among the RES forms, but excessively lower emissions than NRES (Mac Kinnon et al. 2018 ).

Secondly, CSP has lower efficiency standards than PVs, when there is ramping events or no sunlight, but high efficiency can be achieved via thermal energy storage (Denholm et al. 2010 ). The storage and backup systems are necessary for CSP as well (Ellabban et al. 2014 ). Though there are high GHG emissions linked to the life cycle of the CSP plant design, due to the extracted materials used in such infrastructure (Mac Kinnon et al. 2018 ). Thirdly, solar heating is a very effective way of utilizing solar energy for covering basic modern amenities (Ellabban et al. 2014 ).

Apparently, hydro-electricity is a traditional form and a RES yet. Apergis et al. ( 2016 ) found that as the significance of hydropower has become more pivotal for economic growth, as it is a non-polluting source. Unequivocally, hydroelectricity in Europe is confronted with some challenges, such as the creation of hydropower plants in vulnerable ecosystems, the integration in energy markets, and political avoidance to RES infrastructure (Wagner et al. 2019 ; Tomczyk and Wiatkowski 2020 ). Next in order, the NRES forms of NG and LNG are going to be discussed.

NRES include nuclear power and fossil fuels, both of which have negative and positive aspects. Nuclear energy, Footnote 1 for example, is important for the transition to a sustainable future (Nathaniel et al. 2021 ) and can lead to lower CO 2 emissions (Saidi and Omri 2020 ). In addition, unsurprisingly, fossil fuels are also crucial for the economic growth of the previous decades due to their low-cost technology and uncomplicated transformation to electricity (Kanat et al. 2022 ).

Nevertheless, NRES have detrimental impacts on nature and people as well, for instance, Burgherr et al. ( 2012 ) monitored the severity of accidents on NRES energy chains such as fatality rates and subsequent economic losses. Apparently, the GHG emissions are the main drawback of fossil fuels, however can the transitional fuels such as NG and LNG be a compromise?

Regarding the NRES such as NG and LNG there are also advancements and implications that ought to be mentioned. Undoubtedly, the CH 4 emissions are a matter of great concern, especially from old-fashioned ship engines, on contrary, the novel LNG engines can reduce global warming issues yet (Balcombe et al. 2021 ). It has been also reported that volatile organic compounds (VOCs) are emitted by LNG-fueled vehicles that put pressure on air quality (Song et al. 2020 ). There are also alerting issues on terms of safe storage of NG and specifically of LNG due to labour-related accidents in infrastructure and transportation (Elgohary et al. 2014 ). For instance, Not-in-my-backyard (ΝΙΜΒΥ) syndromes are also apparent on such infrastructure.

On the other hand, NG and LNG have a great potential in enabling the transition to renewable fuels, such as the low criteria pollutants in relevance to rest fossil fuels (Mac Kinnon et al. 2018 ). As stated before, the IMO took strict regulations for sulfur and nitrogen oxides (SOx and NOx) (Lindstad and Rialland 2020 ). The most important milestone in LNG usage is the competitiveness in maritime – global and regional – demand by reducing GHGs and enabling port growth (Thomson et al. 2015 ).

1.2 Measuring Total Factor Productivity Towards Carbon Neutrality

Productivity growth has been thoroughly examined under different scientific areas, for instance via non-parametric and linear programming methods. Leading publications were made by Solow, by Farrell’s, and by Charnes-Cooper-Rhodes (CCR model). In more detail, the nexus between productivity growth and technical changes have been explored by (Solow ( 1957 ), while methods of measuring production efficiency scores under the scope of non-parametric analysis were proposed by Farrell ( 1957 ). Moreover, linear programming analysis via DEA considered the relative efficiency of decision-making units (DMUs) (Charnes et al. 1978 ).

There are some typical axioms that should be checked as free disposability, convexity, and ‘’constant-returns-to-scale’’ (CRS). The CCR publication paved the way for CRS, whereas a different approach is the ‘’variable-returns-to-scale’’ (VRS) proposed by Banker, Charnes, and Cooper (BBC model) (Charnes et al. 1978 ; Banker et al. 1984 ).

Briefly, these two models have two distinct characteristics on the matter of proportionality change in variables and referring to the CRS or VRS options. Purporting that the CRS utilizes proportional change on the examined variables, also this model is preferred for constant variables, on the other hand, the VRS is indifferent of proportional change, it is also applicable to non-constant returns to scale (Halkos and Petrou 2019a ). It should be also added that a DMU might represent augmented productivity performance not only due to efficiency improvements, but also because of taking advantage of technological changes and scale economies (Coelli et al. 2005 ).

The theoretical background of Solow on explaining productivity performance was altered by the publication of Caves, Christensen, and Diewert (CCD), who modified the element of time derivative of the production function for advancing index numbers Footnote 2 (Caves et al. 1982a , b ). The reason of altering was done because index numbers involves ‘’a discrete approximation to the time derivative’’, thus the CCD model introduced a similar nomenclature such as the Malmquist productivity index (MPI) honoris causa on Malmquist’s publication (Malmquist 1953 ). Moreover, a phenomenon of great seriousness is that on productivity growth the ‘’Solow residual’’ is looming. This phenomenon shades light on the unexplained output changes due to input changes represented in the ‘’technological change’’ element (Boussemart et al. 2003 ). In general, TFP measures if a DMU reaches the world frontier Footnote 3 through efficiency change (catching-up), and shifts (innovation) on the world frontier due to technological change. However, is there any problem referring to a well-rounded model?

Another aspect that on should bear in mind is what Simar and Wilson stated as “bias by construction” (Simar and Wilson 1998 ; Halkos and Petrou 2019b ; Moradi-Motlagh and Emrouznejad 2022 ). An escape of such a riddle is the running of a bootstrap technique that smooths the outcomes of a DEA model. A bootstrap is subject of Efron’s bootstrap technique that was a simulation of a Data Generating Process (DGP), a DGP is actually the construction of pseudo-data set (Coelli et al. 2005 ; Halkos and Petrou 2019a ).

Malmquist-Luenberger productivity index (MLPI) incorporates the Malmquist notion, however it takes into account distance function in order to enhance input minimization and output maximization (Chambers et al. 1996 ; Boussemart et al. 2003 ; Oh 2010 ; Wang et al. 2020 ). MPI and MLPI have fundamental differences. firstly the choice of distance functions, secondly the incorporation of the economic literature basis of the indices, and thirdly the choice of the index decomposition in a multiplicative or additive Footnote 4 way (Chambers et al. 1996 ; Boussemart et al. 2003 ). The previous reasons constitute the MLPI a suitable way of dealing with undesirable outputs (Chung et al. 1997 ; Jeon and Sickles 2004 ).

A recent review by Chachuli et al. ( 2020 ) illustrated that the importance of DEA on the renewable energy sector and the need to expand the DEA applications in the future. On this basis, we aim to apply a DEA methodology in order to check the productivity performance on the NRES and RES energy sectors. In a similar way, Wang et al. ( 2021 ) evaluated the renewable energy production capabilities based on window DEA and Fuzzy TOPSIS Model. Kim et al. ( 2015 ) found that wind power is the most efficient renewable energy form in Korea, whereas Kolagar et al. ( 2020 ) found that the adoption of RES via hybrid DEA-FBWM might be lead to better planning on the RES energy sector.

Above all, the capabilities of DEA methodology and its extension in copying with undesirable outputs has necessitated the proliferation of studies in operations research (OR) and management science (MS) that try to make this enigma a common practice. For instance, some ways to deal with undesirable outputs are (i) the disregarding of bad outputs on the production function, (ii) the utilization of bad outputs in a way such as input, (iii) handling undesirable outputs with non-linear methodology, and (iv) by managing via structural transformations (Halkos and Papageorgiou 2014 ; Halkos and Petrou 2019a ; Wang et al. 2022 ). In our analysis, two MLPI models utilize electricity generation, either from NRES or RES, but both models have as undesirable outputs the CO 2 and CH 4 emissions. In essence, a common way of interpreting MLPI outcomes is by describing their values: if the TFP take values > 1 (< 1) then we have productivity prosperity (recession), but when = 1, there is stagnation.

2 Data and Methodology

The data were collected from the World Bank Group and Our World in Data for the EU member-states and span throughout the period 1995–2019 and panel data analysis was applied. Furthermore, the present study utilises the European countries as DMUs, to distinguish possible interconnections between the productivity performance of these countries based on their GHGs emissions as in Fig.  1 .

Methodology plan for measuring Malmquist-Luenberger productivity index for NRES and RES

Further, the EU-27 countries have been categorized geographically into four groups as in Fig.  2 , only Estonia has been left out as there was no data availability. In our analysis the DEA formulation (Fig.  1 ) is defined with the following parameters, as inputs: electricity generation (GWh); labour (total); Gross Fixed Capital Formation (GFCF) (current US $). It should be mentioned that the first model is based on electricity generation from NRES (i.e., nuclear, oil, natural gas, and coal), while the second model refers exclusively to RES (i.e., hydro, wind, solar etc.). Moreover, the desirable output is Gross Domestic Product (GDP) (current US $), whereas the two undesirable outputs are: CO 2 (kt) and CH 4 (kt of CO 2 equivalent).

The categorization of EU member-states into four geographical groups

The adoption of RES has risen from 1995 to 2019 as it can be seen on Fig.  3 . The inner circles show the energy generation in 1995, which was basically generated from NRES for most of the cases. On the contrary, the NRES percentage has been reduced as shown at the outer circles that show the energy generation structure in 2019, with some countries, inter alia, Greece, Lithuania, Luxembourg, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden to gain momentum on RES adoption.

Electricity generation percentage of RES (green colour) and NRES (orange colour) in EU-27 countries, the inner circle presents the 1995 levels, whereas the outer circle illustrates the 2019 levels

2.1 Measuring Productivity via the Use of Various Indices

A way of comparing a DMU’s performance is through productivity indices, for instance the present study would apply the two MLPI scenarios. Should these indices undergo a bootstrapping process, then the examination of the interconnections of two differences might be possible. It is important to delve into these differences, thus, firstly the difference between the real and the estimated values of the productivity indices, and secondly the difference between the bootstrapped and the estimated values. Then might be crucial for checking the statistically significant difference between these two distinctions.

2.2 The Production Frontier Technology

If a DMU is set on the boundary of the production curve, then it is technically efficient (Coelli et al. 2005 ). The production function \({S}^{t}\) considers the transformation of inputs \({x}^{t}\in {\mathbb{R}}_{+}^{N}\) , into outputs \({y}^{t}\in {\mathbb{R}}_{+}^{M}\) , where \({\mathbb{R}}_{+}^{N+M}\) is the Euclidean space.

Some axioms referring to the production function should be followed in order to define an appropriate output distance function. Footnote 5 This production set could be defined for the input correspondence \(\left(\forall x\in {S}^{t}\right)\) and for output correspondence ( \(\forall y\in {S}^{t})\) as (Simar and Wilson 1998 ):

Therefore, based on two publications of Farrell ( 1957 ) and Simar and Wilson ( 1998 ), there are efficiency boundaries which are subsets of the above \(X\left(y\right)\) and \(Y\left(x\right)\) , defined as \(\partial X\left(y\right)\) and \(\partial Y\left(x\right)\) : Footnote 6

In such a way, the measures of efficiency of a specific point \({(x}^{t},{y}^{t})\) are:

Probable outcomes of – input and output – efficiency might be supposed by reaching \({\theta }^{t}=1\) and \({\beta }^{t}=1\) accordingly. Having these nexuses in mind, one can reach a feasible production frontier, moreover the above equations are necessary for running a bootstrap technique for alleviating some imminent problems of statistical inferences in DEA.

2.3 Malmquist–Luenberger Productivity Index

The MLPI also follows axioms referring to directional distance function via the use of a broader directional vector \(\left(-{g}_{i},{g}_{o}\right)\) that is parallel to \({g}_{i}=x\) (input/s) and \({g}_{o}=y\) (output/s) (Chambers et al. 1998 ). On the following relations there are – input and output – proportional distance functions accordingly:

where, \({E}_{S\left(t\right)}({x}^{t},{y}^{t})\) is the Debreu-Farell efficiency measure, that is the inverse of Shephard’s distance function (Boussemart et al. 2003 ). The decomposition of MLPI is the following:

This decomposition consists of two square brackets. Firstly, there is the proportional distance function between two time periods (period t to period t + 1) this difference presents the technical efficiency change. Secondly, the other bracket represents the ‘’arithmetic mean’’ of the two other differences, which is ultimately the technological change (Boussemart et al. 2003 ). Footnote 7 Furthermore, this decomposition Footnote 8 is also composed of two new components, i.e., efficiency change (EC) and technological change (TC). Especially the EC component can be further decomposed into: pure efficiency change (PC) and scale change (SC). The element of PC enables the explanation of improvements in core efficiency, while SC represents the returns-to-scale effects (Babu and Kulshreshtha 2014 ).

The estimation of TFP was done through 1000 replications under a bootstrap method introduced by Simar and Wilson, considering time-dependence (Simar and Wilson 1999 ). In addition, the panel data followed directional distance functions based on the literature. Both models have as undesirable outputs the GHGs, either generated from NRES or RES. Table 1 illustrates the descriptive statistics.

In Fig.  4 the averages of the two MLPI models and their components are depicted. The first model includes only NRES, with an average of 2.14%, on the other hand, the second model that utilizes exclusively RES has greater average productivity performance that reached 7.34% progress, almost three times greater than NRES. The countries that showed the greatest RES productivity performance are Cyprus, the UK, Netherlands, France, Ireland, Malta, and Belgium.

Average values of the MLPI and its decomposition during the period 1995 – 2019. The NRES is illustrated in orange colour and the RES in green colour

Additionally, one important aspect is that the countries with higher productivity performance via the RES utilization is based on TC (i.e., innovation, the deep-coloured columns) instead of EC (i.e., catching-up actions, the light-coloured columns). In comparison to Fig.  3 , Fig.  4 illustrates that most of the countries below show a similar performance, as the adoption of RES has significantly impacted some European energy sectors. In contrast, only few countries have high NRES utilization and productivity performance, such as Austria, and Denmark (Table  2 and 3 ).

The TFP is necessary to show how a DMU has performed regarding the utilization of NRES and RES. Table 2 shows how TFP has altered from 1995 to 2019 either in NRES or RES. The greatest NRES TFP for the year 1995 can be found in Bulgaria, Cyprus, and Italy, whereas the lowest TFP is in Austria, Latvia, Slovakia, and Slovenia. However, this pattern changes for the year 2019 as Denmark, Greece, and Ireland showed the greatest NRES TFP, but Hungary and Austria had the lowest performance.

On the other hand, the highest performance for RES TFP for 1995 can be spotted in Slovakia, Netherlands, and Italy, whereas the DMUs with the lowest performance are France, Finland, and Czech Republic. In 2019 the RES TFP greatest performance can be seen in Greece, Ireland, and Malta, whereas the lowest performance is in Lithuania.

A clearer pattern can be observed based on the percentage change of the European regions’ performance, i.e. Northern (N EU), Western (W EU), Central and Eastern (C & E EU), and Southern (S EU). Regarding the NRES, the EU mean has altered from a recession of 0.64% to a progress of 0.09%, or alternatively a percentage change of 0.74%. The greatest positive percentage change was in Western EU (10.18%) followed by Northern EU (6.59%), nevertheless there was a negative pathway for Southern (–0.29%) and Central-Eastern EU (–12.69%).

Similarly, the adoption of RES has a comparable pathway for EU, as the greatest progress has been observed in Western (29.11%) followed by Northern (13.38%), Southern (–4.35%), and Central-Eastern EU (–9.23%). Overall, the adoption of RES has led to a European-level 7% amelioration from 1995 to 2019, meaning a 10-times greater productivity changes in comparison to NRES, a seemingly significant pathway towards green economy and carbon neutrality.

A further juxtaposition between NRES and RES is presented in Table  3 , which depicts the general comparison between these two indices. At first glance, during the whole period of 1995–2019 the MLPI of NRES and RES have 16 and 17 performances above unity respectively, making the electricity generation from RES a bit more important for the TFP performance. Possibly because the technological changes have further expanded innovation on production technology in the EU-27.

By providing more detail, in the NRES MLPI there are 18 periods where technological change surpasses the efficiency changes. Although, the efficiency change is also affected mainly pure efficiency change (18 periods) and then by the scale changes (9 periods). Additionally, there are 19 periods when technological change plays a more important role than efficiency change in the RES MLPI. Even more, the efficiency change is affected and at this model primarily by pure efficiency change (18 periods) followed by scale changes (9 periods).

In essence, the results from Table  3 can be interpreted as that there are more shifts in the technology frontier due to technological change through the form of innovation rather than catch-up actions from efficiency changes. Moreover the number of pure efficiency changes showcases that there are more improvements in the core efficiency rather than returns-to-scale from the scale changes.

4 Discussion

Paving the way for carbon neutrality is being strengthened by solar and wind energy due to high energy return on investment (EROI) Footnote 9 (Stern 2020 ), as well as by advancements in LNG technology (Da Pan et al. 2020 ; Song et al. 2020 ; Balcombe et al. 2021 ). In Table  4 there are several discussed issues for RES and NRES accordingly. Solar and wind energy forms are of the most importance as they are well-established and well-rounded technologies among the RES. Whereas the NG and specifically LNG are going to be discussed based on NRES factor due to its significance in transportation and shipping.

Referring to solar energy the PVs illustrate plenty potentials for enabling rapid energy conversion from sunlight to electricity (Halkos and Gkampoura 2020 ), even for un-electrified developing countries (Ellabban et al. 2014 ). However, there is some controversy for the uncertain – variable or intermittent – operation, and life cycle emissions for PVs (Denholm et al. 2010 ; Mac Kinnon et al. 2018 ).

Another matter of discussion is the employment of auxiliary storage and backup systems for CSP systems, as well facing with GHGs emissions during the infrastructure installation and operation of such systems (Denholm et al. 2010 ; Ellabban et al. 2014 ; Mac Kinnon et al. 2018 ). Solar heating could also provide meaningful way-out of energy insecurity, as it can ameliorate modern lifestyles through the exploitation of the heating energy from the sun (Ellabban et al. 2014 ).

In parallel, wind energy infrastructure ought to be strengthened by auxiliary systems and regulatory incentives except the high-cost disincentives due to dangers towards avian wildlife (Denholm et al. 2010 ; Ellabban et al. 2014 ). On contrary, regarding the wind energy improvements there is no pollution in water and air as well (Ellabban et al. 2014 ), and it is explicitly advantageous for rural communities (Mac Kinnon et al. 2018 ; Halkos and Gkampoura 2020 ).

The transition towards a carbon neutral society and economy can be fostered or frustrated by driving forces. For instance, in EU incentives and disincentives are central for estimating the cumulative impact of NG or LNG on this transition. To exemplify, factors that hamper NG are the EU directives on GHGs commitments and the price volatility; whereas factors that boost are based on plethora of port growth opportunities and state-of-the-art LNG engine design against other conventional fossil fuel engines (Thomson et al. 2015 ).

Moreover, IMO has provided versatile and typically achievable targets, as the GHGs can be halved by 2050 on the shipping sector. Further, IMO has targeted the NOx and SOx (Lindstad and Rialland 2020 ) – a very purposeful action for combating air pollution and mitigating these emissions. In general, it is the whole competitiveness in maritime sector that ought to be fostered through proper regulatory incentives (Thomson et al. 2015 ).

Copying with climate change is in tandem with attaining carbon neutrality. Climate change is gaining momentum in Europe, especially in the Mediterranean basin: where climate change has profound effects on ecosystems (Ali et al. 2022 ; Bednar-Friedl et al. 2022 ). Rising resilience in the Mediterranean broader region and in Europe is unequivocally a hot issue (Schipper et al. 2022 ), hence carbon neutrality is also gaining political, economic, and social debates under the scope of climate change.

This is a reason why SDGs have offered a medium on promoting the fighting of climate change and on providing guidelines for carbon neutrality. On the whole, SDG 13 targets exclusively climate change, and SDG 7 primarily and SDGs 11, 12 indirectly call for clean and affordable energy and typically for carbon neutrality, resilience, and responsible consumption patterns (UN 2016 ; Fotopoulou et al. 2022 ).

To recapitulate, both NRES and RES are part and parcel of the modern energy economics. Specifically when targeting the realization of carbon neutrality objectives. Both of the above energy sources can typically lead to a more clean and sustainable economies and communities. SDGs can be also possible resolution on promoting carbon neutrality.

5 Conclusions and Policy Implications

To recapitulate, either NRES as the discussed—and important transitional fuel—LNG, or the all-around and fully developed RES as the solar and wind energy systems should be taken into account when trying to realize carbon neutrality commitments. Communities and societies can be ushered to more clean and sustainable future through carbon neutrality. A probable answer is the successful conclusion of SDGs (e.g., SDGs 13, 7, 11, and 12) on energy economics via the copying of warfare effects on global or regional economies and energy instability cycles. In parallel, the EGD could be described as a global pathfinder regarding the provision of robust regulatory framework for posing impediments on further climate change and bolstering carbon neutrality.

Furthermore, the two measured productivity indices showed that the adoption of RES ushered to greater performance, even if CO 2 and CH 4 have a powerful impact on TFP. More specifically, the average MLPI of NRES and RES is 2.14% and 7.14% respectively. Regarding the geographical categorization, for both models, the greater performance is attributed to Western EU followed by Northern, Southern, and Central-Eastern EU. Significant is to propose that the Southern and Central-Eastern EU countries ought to expand further their RES technologies in order to ameliorate their productivity performance. Some ways, as discussed above, are the installation of more PVs or wind farms, aiming to grasp their potential in the rising energy markets.

Some policy implications can be targeted on the issues of energy sources diversification, on resource conservation, and the alleviation of energy poverty. The diversification of energy sources would enable to grasp the dynamic potential of RES, moreover this diversification would complement the governmental action on resource conservation, as RES need less natural resources than NRES. The most important, however, is the impact to the society and the adoption of RES would allow more access to affordable electricity and energy security. In essence, RES can be a helping hand for the alleviation of energy poverty in EU, this phenomenon might also help other third countries.

To put it briefly, SDGs can provide useful guidance on dealing with climate change through carbon neutral promotion. The current energy crisis provokes phenomena like energy poverty and economic fragility and volatility. Hence the introduction of resilience in economic, societal, and environmental terms is of great importance. Above all, carbon neutrality might open the way for copying climate change—in short, it is a matter of survival and the base of a clean and sustainable world-to-come.

Data availability

Data available on request due to restrictions, e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and copyright reasons.

Nuclear energy can be categorized to the NRES due to the material used in such power plants, even though it is a low-emission practice (National Geographic 2024 ).

Based on the economic theory, some assumptions referring to index numbers such as that the observed DMUs between two periods are technically and allocatively efficient, with cost-minimizing and revenue-maximizing performances, or rarely based on “constrained optimization” of revenues with cost constraints (Coelli et al. 2005 ).

The world frontier of the present study is EU-27 member states.

Multiplicative measures the relative change, while additive calculates the absolute change.

For more information, please, see: (Shepard 1970 ).

The terms “ \(0<\theta <1\) ” and “ \(\beta >1\) ” represent the feasibility of proportionately reduction input(s) (or increase of output(s)) if \({y}^{t}\) ( or \({x}^{t}\) ) were achieved in an efficient manner (Simar and Wilson 1998 ).

In parallel with the above: \(L\left({x}^{t},{y}^{t},{x}^{t+1},{y}^{t+1}\right)= EC\times TC\) (Boussemart et al. 2003 ).

Equally: \(MLPI{(x}^{t+1},{y}^{t+1},{x}^{t},{y}^{t})= EC\times TC=\left(PC\times SC\right)\times TC\) , it should be mentioned that EC was calculated under CRS, while PC under VRS (Färe et al. 1994 ).

Stern stated that it is ‘’the ratio of useful energy produced by an energy supply system to the amount of energy invested in extracting that energy (Stern 2020 ).

Abbreviations

Banker Charnes and Cooper

Charnes Cooper Rhodes

Data envelopment analysis

European green deal

• Greenhouse gases

Natural gas

Photovoltaics

Sulfur oxides

United Nations

Caves Christensen and Diewert

Constant returns to scale

Data generating process

European Union

Liquified natural gas

Nitrogen oxides

• Renewable energy sources

Total factor productivity

Volatile organic compounds

Gross fixed capital formation

Concentrating solar power

Decision making units

Gross domestic product

Malmquist Luenberger productivity index

Non-renewable energy sources

Sustainable development goals

Variable returns to scale

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Appendix 3: The Simar and Wilson 1999 bootstrap

The idea of this bootstrap is the construction of the confidence intervals based on the assumption that the distribution of the difference between the real unknown index ( \({M}_{S}^{I})\) and the estimated values \({\widehat{M}}_{S}^{I}\) of the index can be approximated by the distribution of the difference between the bootstrapped \({(M}_{S,b}^{*I})\) and the estimated values \({(\widehat{M}}_{S}^{I})\) of the index (Simar and Wilson 1999 ; Halkos and Tzeremes 2015 ). Hence, the confidence interval \((1-a)\) can be between the values \({b}_{a}\) and \({a}_{a}\) as below:

This nexus can be approximated from the values generated \({(\widehat{b}}_{a }and {\widehat{a}}_{a})\) through the bootstrap technique:

Therefore, the bootstrap estimate of the MPI or MLPI or their components can be given as:

As a result, the \(s\) th DMU (on the present study one of the 27 countries) the 1 MLPI is statistically different from unity at \(a\) per cent level if the last equation does not include the value 1. According to Simar and Wilson, a proper B = 2000 replications are needed, or B = 1000 as Hall suggested (Hall 1986 ; Simar and Wilson 1998 ).

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India is committed to achieve the Net Zero emissions target by 2070 as announced by PM Modi, says Dr. Jitendra Singh “India has remained steadfast in its transition towards clean energy achieving the fastest pace of renewable capacity addition amongst all major economies and ambitious transition goals articulated by Prime Minister Modi, in India’s Panchamrit declaration at COP26”: Dr Jitendra Singh India’s energy-mix strategies include a larger shift toward clean energy alternatives, increased manufacturing capacities, energy use efficiency and a policy push for Hydrogen including production-linked incentives, says Dr Jitendra Singh

Union Minister of State (Independent Charge) Science & Technology; MoS PMO, Personnel, Public Grievances, Pensions, Space and Atomic Energy, Dr. Jitendra Singh, has said India is committed  to achieve the Net Zero emission target by 2070, as announced by Prime Minister Shri Narendra Modi.

“We are fully committed to contribute in attaining the United Nations Sustainable Development Goals (SDGs) -  by way of Research and Innovation through international collaboration and partnerships,” he said, participating in the ‘Green Ribbon Champions’ programme in New Delhi today.

Dr Jitendra Singh said, India is set to achieve its short term and long term targets under the Panchamrit action plan, like- reaching a non-fossil fuel energy capacity of 500 GW by 2030; fulfilling at least half of its energy requirements via renewable energy by 2030; reducing CO2 emissions by 1 billion tons by 2030; reducing carbon intensity below 45 percent by 2030; and finally pave the way for achieving a Net-Zero emission target by 2070.

Dr Jitendra Singh said, PM Modi had at the 26th session of the Conference of the Parties (COP26) to the United Nations Framework Convention on Climate Change (UNFCCC) held in Glasgow, United Kingdom in November, 2021, expressed to intensify India’s Climate Action Plan (CAP) by presenting to the world five nectar elements (Panchamrit) of India’s climate action plan. He said, apart from a five-pronged target for India and its commitment to Net-Zero emissions by 2070, PM Modi also asserted the need to follow a sustainable lifestyle and emphasised on the idea of making ‘Lifestyle for Environment’ (LiFE) a global mission through bolder steps by the global clean energy fraternity.

Dr Jitendra Singh said, the Mission Innovation (MI) and International Solar Alliance, spearheaded by PM Modi, were announced at COP21 in 2015, when the United Nations conferred on him the ‘Champions of Earth Award 2018.’

The term ‘Mission Innovation’ was coined by PM Modi. Mission Innovation (MI) is a global initiative of 23 countries and the European Commission (on behalf of the European Union) to accelerate the Clean Energy revolution and progress towards the Paris Agreement goals and pathways to net zero. India is a founding member of Mission Innovation.

The first phase of Mission Innovation (MI) (2015-2020) was announced at COP21 on November 30, 2015. In the first phase of Mission Innovation, India led three MI Innovation Challenges, such as Smart Grids, Off Grid Access to Electricity and Sustainable Biofuels, and hosted many workshops. 

Dr Jitendra Singh said, Government of India through Public-Private Partnerships is ensuring the funding for clean energy innovations as envisioned under Mission Innovation 2.0. He said that the Clean Energy Ministerial (CEM) setup has been able to provide a unique opportunity for India to showcase its contribution to clean energy development nationally & internationally and quoted some major CEM initiatives which includes CEM’s Global Lighting Challenge (GLC) campaign, Street Lighting National Programme, Unnat Jyoti by Affordable LEDs for All (UJALA) programme and the ‘One Sun-One World-One Grid’ Initiative which was first floated by the Prime Minister to harness tremendous potential of solar energy.

Referring to the G20 Summit earlier this month, Dr Jitendra Singh said the New Delhi Declaration committed itself to implement India’s initiative of 'Lifestyle for Environment Mission’ (LiFE) and promote Artificial Intelligence towards achieving the UN SDGs. By adopting the 'Green Development Pact,' G-20 has also reaffirmed its commitments to sustainable and green growth.

The Union Minister said that the Global Biofuels Alliance (GBA) to be led by India, Brazil and the United States, as leading biofuel producers and consumers, will greatly help achieve the MDG Goals of India turning Net Zero by 2070.

“GBA is a landmark achievement agreed to by the leaders of Singapore, Bangladesh, Italy, USA, Brazil, Argentina, Mauritius and UAE, at the initiative of PM Modi on the sidelines of the G20 Summit. GBA aims to serve as a catalytic platform, fostering global collaboration for the advancement and widespread adoption of biofuels,” he said.

Dr Jitendra Singh said India stands at the forefront of addressing the global challenge of Climate Change and has committed to an ambitious Nationally Determined Contributions (NDCs) of reducing emission intensity by 33-35% in 2030 against the levels of year 2005.

“India has remained steadfast in its transition towards clean energy, achieving the fastest pace of renewable capacity addition amongst all major economies and ambitious transition goals articulated by Prime Minister Modi, in India’s Panchamrit declaration at COP26,” he said.

The Union Minister said that the last nine years have witnessed the Indian crusade against climate change. “We have already achieved our commitment of 40% energy production from renewable sources, way ahead of the 2030 Paris Agreement target,” he said.

The Minister reiterated India is implementing the largest Renewable Energy (RE) expansion program in the world, envisaging a 5-fold increase in the overall RE capacity.

He added that apart from the thrust on renewable energy from solar and hydel, the Prime Minister announced major strides in Hydrogen energy in his Independence Day Address from the ramparts of the Red Fort on 15th August 2021. India also launched the National Hydrogen Energy Mission to enable cost competitive green hydrogen production.

“India’s energy-mix strategies include a larger shift toward clean energy alternatives, increased manufacturing capacities, energy use efficiency and a policy push for Hydrogen including production-linked incentives. In addition, the emerging technologies like 2G Ethanol Pilot, Comfort climate box for tropical regions, Hydrogen Valleys, Heating and cooling virtual repository, are all on the table,” he said.

Dr Jitendra Singh said India has developed a roadmap and a strategy for a Bio-based economy which is inching towards 150 billion USD by the year 2025. Department of Biotechnology has been supporting R&D innovations in Advanced Biofuels and ‘Waste to Energy’ technologies. India has established 5 Bioenergy Centers with an interdisciplinary team working on advanced sustainable biofuels using modern biotechnology tools.

“This will facilitate infrastructure for Bio-manufacturing of low-carbon bio-based products. Sustainable biofuels play a key role in reducing Green House Gas (GHG) emissions from the transport sector,” he said.

Dr Jitendra Singh said, India is among the few countries in the world to design a Cooling Action Plan (CAP) with a long-term vision (spanning a 20-year period from 2017-18 to 2037-38) that addresses cooling requirements across sectors.

“CAP identifies possible actions to reduce cooling demand arising from residential and commercial buildings, cold chains, etc., covering aspects of building design and technological innovations that do not compromise on energy efficiency,” he said.

Recalling that he launched India’s first truly indigenously developed Hydrogen Fuel Cell Bus developed by KPIT-CSIR in Pune on 21 Aug 2022, Dr Jitendra Singh said the painstaking efforts undertaken over the last two years since the Prime Minister had announced India’s intention to create a dedicated mission for Green Hydrogen, culminated in the announcement of the National Green Hydrogen Mission with a budgetary outlay of about 2.4 billion dollars in January this year.

He said that a draft R&D Roadmap for Green Hydrogen Ecosystem in India has been released. A PPP framework for R&D called the Strategic Hydrogen Innovation Partnership or SHIP will be facilitated under the Mission. 

“India is uniquely poised to emerge as a prominent global leader in production of Green Hydrogen not just on the basis of its abundant renewable energy resources and the benefits of one of the world’s lowest costs of regeneration, but also because of its R&D ecosystem and the framework designed for R&D in cross-cutting sectors of hydrogen production, transport, electrolyze manufacturing, support infrastructure, fuel cell EVs, storage and utilization,” he said.

Dr Jitendra Singh further said that nearly 9% share of electricity is likely to be contributed from India’s nuclear sources by the year 2047. The Department of Atomic Energy aims to achieve 20 GW capacity of nuclear power generation by the year 2030 which will be a major milestone placing India as the third largest producer of atomic energy in the world after the USA and France.

“Credit for this rapid stride goes to Prime Minister Narendra Modi, who for the first time after Independence took the decision of approving 10 reactors in fleet mode in a single order and allowed nuclear installations to be developed under joint ventures with PSUs. As a result, today India is sixth largest in the world in the number of reactors that are functional and second largest in the total number of reactors including those under construction,” he said.

Dr Jitendra Singh mentioned sustainable practices in fisheries, marine research, coastal tourism and renewable energy generation. He said, by harnessing the potential of the Blue Economy, we can ensure the well-being of our oceans while driving economic development in a sustainable and responsible manner. We are also concerned about increased plastics and microplastics in our oceans, another important area to focus as it enters into our food chain as several marine organisms consume them.

Dr Jitendra Singh said, the National Research Foundation (NRF) Bill, 2023, passed by the Parliament in the last monsoon session, will promote the culture of research and innovation throughout India’s universities, colleges, research institutions, and R&D laboratories at a total estimated cost of Rs. 50,000 crores during five years and this will give further impetus to Clean energy research in India and Mission Innovation. As much as 70% of its funding will come from non-governmental sources, he said.

Dr Jitendra Singh said, despite efforts by countries to check and reduce emission of greenhouse gases, the global average temperature in 2100 is expected to rise to around 2.1°C above pre-industrial levels. The Minister pointed out that this falls short of the goals stipulated in the Paris Agreement, which calls for limiting the global temperature to 1.5°C above pre-industrial-era levels by the end of the century.

Dr Jitendra Singh said, Precision irrigation, Innovative clean water technologies, such as water purification systems, desalination techniques, and wastewater treatment technologies are to be further augmented and implemented. 

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How Countries Are Planning to Produce Hydrogen

As of last month, 61 countries have published a national hydrogen strategy.[i] Continuing from the authors’ previous blog on what these strategies tell us about hydrogen trade, this...

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Blog by Anne-Sophie Corbeau & Rio Pramudita Kaswiyanto • June 7, 2024

As of last month, 61 countries have published a national hydrogen strategy. [i] Continuing from the authors’ previous blog on what these strategies tell us about hydrogen trade, this blog is a comparative analysis of the various production pathways each nation is considering. It utilizes CGEP’s National Hydrogen Strategies and Roadmap Tracker , which gathers official documents published by governments.

Knowing what energy sources countries plan to use for hydrogen production is useful because it shows that production pathways envisaged are more varied than just electrolysis and steam-methane reforming with carbon capture, utilization, and storage (CCUS).

Dogmatic Versus “All-of-the-Above” Approaches

Each country’s specific conditions—geographical location, quality and quantity of natural resources, and current energy market—affect their hydrogen strategy. All strategies aim to develop renewable hydrogen, in line with studies showing that two-thirds of planned hydrogen production capacity by 2030 could come from renewable sources. [ii] But only 13 countries plan to rely solely on wind and solar, including potential exporters such as Chile, Oman, and South Africa as well as a few European countries. Meanwhile, a handful of countries—Brazil, Canada, Colombia, Indonesia, and the United States—have a more technology-neutral approach and plan to take advantage of the wealth and variety of their resources with six to seven options (see Table 1). Other strategies include between three and five options, such as other renewable sources, nuclear, by-product, natural, and low-carbon hydrogen.

Renewable Hydrogen

Every single national strategy mentions at least one form of renewable energy to produce electrolytic hydrogen. Wind and solar are the most widely mentioned—in 55 national strategies out of 61—with 42 countries using both sources, seven using only wind, and six only solar. Six countries refer to “renewable electricity” without more details, while Germany mentions renewable energy sources on top of offshore wind.

Meanwhile, hydro-electricity appears in 21 strategies, allowing for a more continuous use of electrolyzers. This includes Brazil and Canada, which already have around 60% of hydro in their electricity mix, as well as most Latin American countries, which also boast significant hydro potential. Norway and Romania are the only European countries that refer to hydro. Other countries planning to use hydro include Kazakhstan, Kenya, and Russia. Finally, using geothermal energy to power electrolyzers is envisaged by Colombia, Costa Rica, Ecuador, Indonesia, Kenya, and New Zealand.

Many countries planning to produce hydrogen using electrolysis are anticipating the impact on water resources. [iii] Water used for electrolysis should be deionized water. If produced from freshwater, this may result in a conflict between hydrogen production or other uses, such as agricultural or municipal. Countries experiencing water stress are often located in Africa, but Ecuador and Portugal are also paying attention to this. These countries often aim to use seawater desalination to supply the water, which may also solve the problem of water availability for the population. Morocco and Portugal believe that additional investment for desalination is insignificant compared to the total capital required for the hydrogen ecosystem. Portugal is also considering the use of water from wastewater treatment plants.

Low-Carbon Hydrogen

Despite some countries’ strong preference for renewable hydrogen, low-carbon hydrogen based on fossil fuels is actually mentioned by around half of the strategies. Most plan to use steam-methane reforming with CCUS; however, countries such as Austria, Canada, the Czech Republic, Germany, Mauritania, and Turkey also mention pyrolysis, [iv] albeit with some caveats regarding the technology readiness. Gas producers—Australia, Canada, Indonesia, Kazakhstan, Malaysia, Norway, Russia, and the UAE—have significant gas resources and carbon storage potential, and plan to continue using their gas resources in a hydrogen economy. However, low-carbon hydrogen is also mentioned in some EU countries’ plans, including Belgium, the Czech Republic, Hungary, the Netherlands, and Poland. Germany mentions its production in a limited way, while the UK confirms its interest. In plans, producers often argue that these could be a suitable short-term source to quick-start the market, mainly because of their lower costs—compared to current renewable hydrogen costs—and the ability to produce at scale and in sufficient quantities. While EU countries agree on low-carbon hydrogen’s potential to quick-start the market in their strategies, they often insist on its transitional aspect.

A handful of countries such as Australia, Colombia, Poland, and Russia are also exploring the possibility of coal gasification due to their existing coal reserves and production. However, none of the four largest coal producers—China, India, Indonesia, and the US—is considering coal-derived hydrogen.

Other Sources of Hydrogen

Of the 17 countries considering nuclear, almost all have operating nuclear power plants, except for Indonesia and Poland. Only a few are European, notably France, which has been a long proponent of this production method. Meanwhile, five countries also recognize by-product hydrogen (China, Canada, Finland, India, and South Korea) and three are investigating natural hydrogen (Brazil, France, and Morocco).

Although bio-hydrogen [v] is not getting as much media coverage as electrolytic hydrogen based on wind and solar, various biomass-based technologies (including gasification, pyrolysis, or biomethane reforming) are mentioned by 21 countries, while another two (Germany and the UAE) mention it as a limited source. India’s plan mentions that biomass has the potential to achieve scale and low costs while delivering a continuous hydrogen output.

Ambitions in Projected Hydrogen Production and Electrolyzer Capacity

All but 12 of the 61 countries include production targets for hydrogen quantity or electrolyzer capacity in their plans (see Table 2). Meanwhile, 44 countries indicate a 2030 target, highlighting the preference to give medium-term visibility to the industry. In contrast, there are fewer 2040 and 2050 targets (23 and 24, respectively). The highest 2030 production targets are found in the US and India, up to 10 million tons (Mt) each,  while the US plans to be one of the largest producers by 2050 (50 Mt). Announced electrolyzer capacity targets amount to around 125 gigawatts (GW) by 2030; this number reaches almost 225 GW if one translates the renewable hydrogen production targets into electrolyzer capacity. [vi] Meanwhile, announced hydrogen production would reach around 41 Mt by 2030.

One can question the realism of these forecasts. Electrolyzer installed capacity was 1.1 GW as of October 2023. [vii] Electrolyzer manufacturing capacity was 32 GW in 2023, and expected to reach 50 GW in 2024 and 75 GW in 2025, which could support the expansion. [viii] But progress in terms of projects reaching final investment decision (FID) has been slow: only 7% of the 45 Mt of planned renewable and low-carbon hydrogen projects reported by the Hydrogen Council had taken FID as of October 2023. [ix] BNEF’s most recent analysis also pointed to these shortcomings, forecasting that supply would reach only 16.4 Mt by 2030. [x]  

Additionally, it is uncertain whether sufficient renewable capacity could be developed in time without cannibalizing resources for the power sector. Countries have very different starting points when it comes to material renewable sources of electricity: only a handful of countries with hydrogen plans have renewable shares above 90%—Costa Rica, Norway, Paraguay, and Uruguay [xi] —with countries such as Brazil and New Zealand having shares above 80%. While many countries are counting on their significant renewable energy potential, it is often not yet tapped. And, as noted, their objectives will also have to take into account developing renewables to decarbonize their power mix. Figure 1 shows the ambitions of selected countries for 2030, highlighting the gap between existing and planned renewable capacity dedicated to hydrogen production. Similarly, low-carbon hydrogen relies on sufficient carbon capture and storage capacity to be developed in a timely manner.

[i] Anne-Sophie Corbeau and Rio Pramudita Kaswiyanto, “National Hydrogen Strategies and Roadmap Tracker,” Center on Global Energy Policy, Columbia University, May 2024, https://www.energypolicy.columbia.edu/publications/national-hydrogen-strategies-and-roadmap-tracker/ . Three countries (Estonia, Kazakhstan, and Uzbekistan) have been added since the last edition of the tracker was published in May, and were not included in the previous blog.

[ii] Hydrogen Council, Hydrogen Insights 2023 , December 2023, https://hydrogencouncil.com/wp-content/uploads/2023/12/Hydrogen-Insights-Dec-2023-Update.pdf.

[iii] Water is also needed for steam-methane reforming.

[iv] Pyrolysis enables the splitting of methane into hydrogen and solid carbon.

[v] Yushan Lou et al., “The Potential Role of Biohydrogen in Creating a Net-Zero World,” Center on Global Energy Policy, Columbia University, January 9, 2023, https://www.energypolicy.columbia.edu/publications/the-potential-role-of-biohydrogen-in-creating-a-net-zero-world/.

[vi] In cases of a range of forecasts, the average was used.

[vii] Hydrogen Council, Hydrogen Insights 2023.

[viii] Martin Polly, “’Severe overcapacity’ | The Global Supply of Electrolysers far Outstrips Demand from Green Hydrogen Projects: BNEF,” Hydrogen Insights, March 28, 2024, https://www.hydrogeninsight.com/electrolysers/severe-overcapacity-the-global-supply-of-electrolysers-far-outstrips-demand-from-green-hydrogen-projects-bnef/2-1-1618327 .

[ix] Hydrogen Council, Hydrogen Insights 2023 .

[x] BNEF, “Hydrogen Supply Outlook: A Reality Check,” May 2024, https://about.bnef.com/blog/hydrogen-supply-outlook-2024-a-reality-check/ .

[xi] Including thermal biomass.

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