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The present paper reviews the green hydrogen uptake policies in the United States and the European Union. While the EU has a rather broad public support strategy that envisages the entire hydrogen value chain, the U.S. policies are more focused on hydrogen research hubs and subsidies having electric vehicle applications. From the experience of the solar and wind power rollout of the past three decades, we know that the subsidization schemes of the end product play an important role in the transition to renewables. These kinds of subsidization schemes are still lacking for clean hydrogen both in the EU and the U.S. This paper provides a brief overview of selected subsidization schemes from other renewables markets, which are then applied to define an exemplary subsidization path to empower large-scale growth in a market for green hydrogen. Here is proposed a long-term subsidization scheme – deemed as an adequate strategy for the expansion of clean hydrogen in an electricity mix. The suggested exemplary subsidization path recommends shifting from feed-in tariffs to direct marketing with market premiums once a certain threshold is reached, while both can be flanked by the issuance of a Guarantee of Origin for green hydrogen so that there is one market for hydrogen and another market for the certificates. • -A long-term subsidization scheme for the rollout of green hydrogen in the electricity mixes is proposed. • -An exemplary subsidization path to empower large-scale growth in a market for green hydrogen is drafted. • -U.S. and EU are used as case studies to capture lessons for subsidization of green hydrogen economy uptake. • -The U.S. has more centralized green hydrogen rollout policies, whereas the EU has a wider policy toolkit. • -Shifting from FIT to direct marketing with market premiums is key once a certain pre-defined threshold is reached. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pretreatment is a critical process for biohydrogen production from biomass. • A distinction should be made between the initial pH and the pH of the process. • Dissolved bio-H 2 concentrations are not always in equilibrium with the gas phase. • The combined fermentation methods offer advantages compared to dark fermentation. • Iron catalysts are attractive due to high abundance and relatively low toxicity. Biohydrogen is a green energy carrier with the potential to reduce our dependency on fossil fuels. The production of biohydrogen from lignocellulosic materials may be low cost, but commercial production has not been realized. The issues to be addressed include screening and construction of microorganisms that meet industrial requirements, optimization of bioreactor designs, and novel hybrid fermentation systems such as combining dark fermentation (DF) with methanogenic systems and doping nanomaterials to increase biohydrogen yield. This paper focuses on the biohydrogen production, highlighting various fermentative bioreactor types, influential factors in dark fermentation, and pretreatment techniques. Besides, current strategies to produce biohydrogen, including synthetic biology approaches and metabolic engineering, are assessed for their potential to contribute to net zero carbon emission. Pretreatment of biomass using dilute acids is the most common technique to destroy the complicated structure of lignocellulosic biomass. Mesophilic temperatures (37 °C), HRT of 72 h, and pH near 7.0, are recurrent parameters of biomass fermentation. The combined approach of electro-fermentation and dark fermentation produces a higher yield of biohydrogen compared to the single dark fermentation. Furthermore, additives (mainly iron-based materials) have been investigated to improve the efficiency of biomass pretreatment as well as biohydrogen production. Finally, this paper points out that the vigorous development of novel engineered strains is an important part of achieving commercial-scale production of biohydrogen. Overall, considerable efforts are needed from both technical and managing aspects to achieve a full-scale application of biohydrogen production to make this technology economical, efficient and sustainable. [ABSTRACT FROM AUTHOR]

• The proposed joint demand response and dynamic pricing strategies can make the net grid demand curve smoother and have practical significance. • The dynamic pricing strategy helps to realize the joint dispatch of multiple demand response resources in the region and alleviate the grid pressure during peak hours. • A two-layer optimization model is proposed to not only realize the economy of joint operation of multiple energy stations, but also make the net grid demand curve smoother through joint demand response. In recent years, countries around the world have made great efforts to coordinate energy green and low-carbon development and energy supply. In this context, it is of great significance to build energy stations that can greatly absorb renewable energy. The coordinated operation of multi-energy stations in the region can expand the role of energy stations and strengthen energy complementarity between regions. Aiming at the problem of energy interaction and coordinated operation of multi-energy stations in regional integrated energy system, this paper proposes a two-layer scheduling optimization model, which can effectively reduce the system operation cost and carbon emissions. At the same time, considering the joint demand response, it can effectively reduce the peak power supply pressure of the power grid. In addition, in order to ensure the accuracy and efficiency of the solution, this paper improves the traditional NSGA-II algorithm, greatly improving the overall performance of the algorithm. Finally, the simulation analysis is carried out through an example. The results show that the operation optimization model proposed in this paper can effectively reduce the total energy cost of the system, reduce carbon dioxide emissions, and reduce the peak pressure of the power grid. The coordinated operation optimization method is effective and feasible. [ABSTRACT FROM AUTHOR]

The changes in energy use and energy-mix are important in ensuring the implementation of the sustainability goals, especially those related to the climate change. Even though there have been attempts to analyse the energy-related greenhouse gas (GHG) emission in the European Union, there is still a lack of research that considers structural change, fossil energy consumption, and energy-mix simultaneously. In this context, the present paper addresses the growth in energy-related greenhouse gas (GHG) emissions in the European Union (EU) agriculture. The index decomposition analysis based on the logarithmic mean Divisia index is implemented over the period of 2004–2021. The change in the GHG intensity at the EU level is factorized with respect to the structural effect and those related to the energy intensity, the share of the fossil energy, and carbon factor for the fossil energy. The results suggest that a decline in the energy intensity appeared as the main driver pushing the energy-related GHG emission intensity down in the EU agriculture. This calls for further actions directed towards the increase of the use of renewables. [ABSTRACT FROM AUTHOR]

In this paper, we suggest how India can become atmanirbhar (i.e. self-reliant) in development of green energy technologies- solar, battery storage and green hydrogen, given its stated commitments in clean energy. First, we introduce a generalized policy design – a set of ten policy measures: seven necessary and three sufficient- with the former being on the supply side and the latter on the demand side. The paper argues that while the ideal policy mix would be technology dependent, based on technology maturity and the strategic positioning of the country, it should draw both from supply side (i.e., for domestic development) and demand side (i.e., for scale deployment). Then, we suggest the required individual strategies for each of the three technologies- such as a manufacturing focused policy for solar solely to develop competence in manufacturing; a bottom-up, manufacturing-led policy for battery storage with focus on deployment in parallel with technology development; and traditional top-down, research-led strategy for green hydrogen by entering via development through supply side and complementing via deployment through demand side. Finally, we compare these with the existing domestic energy industrial policies as well as those prevalent in other jurisdictions such as the US and China. • Create self-reliance for India in green energy technologies. • Develop generalized policy design with supply and demand side measures. • For solar, manufacturing focused policy for competence solely in manufacturing. • For battery storage, bottom-up manufacturing-led policy with deployment alongside development. • For green hydrogen, top-down research-led strategy where deployment follows development. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goal (SDG) that emphasizes sustainable and affordable energy presents a practical pathway to achieve sustainable growth. Moreover, this goal aligns with SDG 13, which focuses on climate action. Nonetheless, it is important to consider factors such as political and financial risk, as they can influence climate action efforts and the renewable energy sources adoption. Hence, this study broadens the conversation surrounding CO 2 emission reduction and the achievement of Sustainable Development Goals (SDGs). It accomplishes this by investigating the interplay between renewable energy, political risk, financial risk, and CO 2 emissions in Finland spanning from 1990/Q1 to 2021/Q4. Notably, this research distinguishes itself through its exploration of these complex connections, employing a wavelet-based quantile methodology. Unlike traditional quantile regression, which can only reveal connections between variables within each quantile, the wavelet-based quantile approach enables the simultaneous capture of correlations across quantiles and periods. The results show that in the long-term and across all quantiles (0.01–0.99), energy efficiency, specifically gas and oil efficiency, lessens CO 2. In addition, low financial and political risks improve ecological quality by lessening CO 2 across all periods and quantiles (0.01–0.99). The paper outcomes serve as a basis for formulating a policy framework aimed at achieving SDG 7 and SDG 13 in Finland. • We explore the impact of energy efficiency and country risk on CO 2 emissions. • The role of renewable energy and economic growth are also analyzed. • The Time series data of the Finland is used from 1990/Q1 to 2020/Q4. • Various wavelet-based quantile correlation is applied. • Renewable energy, oil and gas efficiency decrease CO 2 emissions in each quantile and across periods. [ABSTRACT FROM AUTHOR]

• Construct indicator systems for green finance and clean energy. • Investigation conducted on coupling coordination relationship between green finance and clean energy systems. • Influencing factors of coupling coordination degree explored using fsQCA method. • Results reveal imbalance in coupling coordination degree for GCS in China. • Identification of three pathways for enhancing coupling coordination degree. Investigating the integrated development between green finance and clean energy has significant implications for achieving structural energy transformation and sustainable financial development. This paper aims to explore the path toward achieving a positive interaction between green finance and clean energy. To achieve this, a coupling coordination degree model for the green finance – clean energy system is constructed. Additionally, limited attention has been paid to the interconnectedness of multiple factors with coupling coordination. Hence, we introduce a fuzzy set qualitative comparative analysis (fsQCA) approach to explore diverse pathways to enhance coupling coordination. Empirical results indicate that from 2011 to 2020, the degree of coupling coordination between green finance and clean energy in China improved from 0.3341 to 0.4718. However, it remains on the verge of an imbalanced level. Furthermore, the regional coupling coordination degree exhibits uneven development, with a tendency towards high-value clustering. The coordinated development paths of different provinces are driven by various configurations of conditions. Based on these findings, this study suggests that provinces should formulate policies tailored to their local characteristics and condition configurations, and effectively promote the coordinated development of green finance and clean energy in their respective regions. This study improves the comprehension of the coordinated development between green finance and clean energy at the provincial level in China. The employed fsQCA technique offers a novel approach for investigating developmental pathways of coupling coordination, bearing significance in promoting a mutually beneficial and harmonious relationship between green finance and clean energy. [ABSTRACT FROM AUTHOR]

With its strong ties to the European Union, Germany is well-positioned to assist many countries achieve sustainability goals. Therefore, the main objective of this paper is to examine how renewable energy intensity and renewable energy share improve environmental sustainability, the load capacity factor (LCF) in Germany from 1970 to 2018. To this end, the study uses the autoregressive distributed lag model, with the effects of the Kyoto Protocol included as a dummy variable. The study contributes to the literature by considering and testing for the first time the impact of renewables on the LCF in terms of intensity and share for Germany. The empirical results show that (i) economic growth lowers the LCF faster in the early stages of economic development than in later periods; therefore, the environmental Kuznets curve hypothesis is valid in Germany. (ii) A 1% upsurge in renewable energy intensity has no statistically significant effect on the LCF, while a 1% rise in the share of renewables in the total energy mix increases LCF by 0.48%. (iii) A 1% rise in urbanization reduces the LCF by 6.52%. (iv) A 1% augment in human capital enhances the LCF by 3.49%. These outcomes suggest that the German government should use human capital development and increasing the share of renewable energy as policy instruments to achieve the sustainable development goals. [Display omitted] • The EKC hypothesis is valid for Germany. • Renewable energy intensity has no influence on the load capacity factor. • A higher renewable share in the energy mix is associated with an improvement in LCF. • Human capital plays a key role in solving environmental challenges. • The urbanization process has a devastating effect on the environment. [ABSTRACT FROM AUTHOR]

Green finance is essential for supporting the development of green, low-carbon projects and preventing environmental and climate financial risks. Investigating and studying whether and how it can aid in implementing energy transformation is worthwhile. This paper uses 30 provinces from 2004 to 2020 as research samples, the entropy method to construct a green finance measurement system, and the weighted multidimensional vector angle index method to construct an energy low-carbon transformation index based on the actual situation of green finance in China and the existing theoretical research basis. We systematically analyzed each province's spatiotemporal evolution characteristics of green finance and energy structure transformation by constructing a fixed effect model, a GMM model, an intermediary effect model, and a spatial Dubin model. We empirically tested the effect direction, internal mechanism, spatial spillover, and regional heterogeneity of green finance development (GFD) on energy structure transformation (EST) to investigate the low-carbon transformation of energy structures. The findings indicate the following:(1) Green finance plays a positive role in promoting the transformation and development of China's energy structure. (2) The influence of green finance development on the transformation of China's energy structure has significant spatial spillover, exhibiting "local and neighborhood" reciprocity. Through the spatial spillover effect, green finance can encourage the low-carbon transformation of local energy structures and increase the level of low-carbon transformation in neighboring areas. (3) The improvement of the level of green technology innovation and the intensity of environmental regulations are essential channels for green finance to support the low-carbon transformation of the energy structure, and regional heterogeneity will alter this transmission mechanism. The findings of this study provide empirical evidence for the vital role of green finance in achieving the double dividend of economic growth and low-carbon emission reduction. This study provides a new perspective for constructing a green finance system and advancing the dual-carbon objective and a resource for formulating effective regional energy transformation policies. [ABSTRACT FROM AUTHOR]

Pine needles are a major cause of forest fires in the western region of the Himalayas in India. There is a need to explore avenues for utilizing this forest bio residue. Pine needles in the form of briquettes are an important source of renewable energy as it has low carbon emissions, low-cost technological interventions, easy accessibility and direct community benefits. The purpose of this paper is to list, classify and rank the enablers supporting energy generation using briquettes made from dried pine needles. This study ranks the enablers by using the fuzzy Analytic Hierarchy Process which is a multi-criteria decision-making approach. This paper finds that technological enablers are key to such interventions using dried and fallen pine needles for energy generation. The second category is environment-related enablers that directly impact the environment, followed by the market-related factors including good demand for the bio briquettes by the industry, viable fuel linkage, and a sense of business approach among the main actors. Economic enablers are next in importance, including factors such as employment generation, additional income, and self-sufficiency making such initiatives attractive enough to be adopted by people. Government and lastly, social and community-related enablers are next in priority for such projects. This is the first research that recognized, classified as well as ranked these enablers to pine needle-based energy generation in Uttarakhand in achieving United Nations' Sustainable Development Goals for 2030 such as climate action and affordable and clean energy. • To categorize and assess the major enabling factors supporting energy generation using Pine needles. • AHP with Fuzzy logic approach used for prioritization of barriers. • Managerial and practical implications for these enablers. • Technology related enablers are key to pine needle based energy generation. • Low maintenance of machinery has topmost global ranking as enabler. [ABSTRACT FROM AUTHOR]

Although considerable literature has focused on the energy poverty reduction effects of green finance, the context of economic policy uncertainty has been paid little attention. Based on Chinese provincial panel data from 2004 to 2020, this paper studies the relationship between green finance and energy poverty the using system generalized method of moments and examines the impacts of economic policy uncertainty by the panel vector autoregressive model. Our findings indicate that the development of green finance effectively alleviates energy poverty in both the short and long terms. From the evolution path perspective, green finance mainly focuses on improving availability and cleanliness in the initial stage, subsequently, efficiency and completeness. In addition, the rising uncertainty in economic policy will decline clean energy investment, thereby hindering the energy poverty mitigation effects of green finance. The government should maintain the development of green finance through fiscal and tax measures while strengthening financial regulations. The government should also try to consider the efficiency and completeness of clean energy supply in the initial stage of an energy poverty eradication strategy. Furthermore, reducing the uncertainty of economic policy and guiding private investors and state-owned investors into clean energy transition are of great importance. [ABSTRACT FROM AUTHOR]

In today's world, environmental concerns are at the forefront of countries' considerations, along with economic priorities. In this context, countries face the challenge of developing and directing their investments in clean energy technologies to achieve the Sustainable Development Goals, particularly affordable and clean energy. Does the development and diffusion of clean energy technologies contribute to improving environmental quality? This study addresses this question by examining the effects of clean energy technologies on sustainable environmental quality as expressed by the load capacity factor (LCF). In this context, the study explores the United States (US), as the largest economy and one of the leading countries investing in clean energy, considering the role of human capital, urbanization, and economic growth. To this end, this paper applies the novel augmented autoregressive distributed lag (ARDL) model to annual data from 1974 to 2018. The long-term empirical results show that (i) Clean energy technologies have no effect on the LCF; (iii) human capital supports environmental quality; (iv) urbanization has an adverse effect on environmental quality; (v) increasing economic growth worsens environmental quality. Overall, the empirical results suggest that while clean energy technologies should support sustainable environmental quality, this is unfortunately not the case in the US. Therefore, the US policymakers need to take improvement actions to ensure that clean energy technologies contribute to sustainable environmental quality and that the community reaps the environmental benefits of human capital. [Display omitted] • Applies the Augmented ARDL approach with smooth structural shifts. • Tests the impact of clean energy technologies on environmental quality. • Clean energy technologies do not affect the environmental quality. • Income and urbanization reduce the load capacity factor. • Human capital improves environmental quality. [ABSTRACT FROM AUTHOR]

The rapidly increasing demand for electrical energy and the depletion of fossil fuels ignited research in renewable energy generation and management. Thermoelectric generation (TEG) systems as a green energy resource are designed to recover waste heat recovery. Concentrated solar tackles the disadvantages of low power generation efficiency. This paper investigates the feasibility of using machine learning (ML) based MPPT technique, to harvest maximum power of a centralized TEG system under various operating conditions. The environment-dependent issues such as the non-constant distribution of heat, loss to the heat transfer coating between sinks and sources, and mechanical faults create non-uniform current generation and impedance mismatch causing power loss. In this article, we have proposed a feed-forward neural network (FNN) trained by a novel flow direction algorithm (FDA) with a tuned PID controller to harvest the energy under non-uniform temperature gradient conditions. The non-uniformity of physical conditions generates multiple local maxima's (LMs) in electrical characteristics of TEG systems. Common gradient-based MPPT techniques are unlikely to track true GMPP. As a solution to this non-linear problem, novel FNN-FDA is tested under non uniformly distributed temperature (NTUD) over heat source surfaces under varying load and temperature conditions. In this study, certain contributions to the field of TEG systems were made by tackling the issues such as GMPP tracking, low efficiency, and oscillations around GMPP. The results are compared to the particle swarm optimization (PSO), Barnacle mating optimization (BMO), and grey wolf optimization (GWO) algorithm. Five experiments under different weather conditions are performed. Results are validated and proved with experiments and MATLAB/SIMULINK. The experimental results demonstrated that FNN-FDA performs significantly better when compared with other control techniques. • This paper presents Flow Direction Optimization (FDO) algorithm trained using Feed Forward Neural Network (FDA-FNN) based novel intelligent techniques for Maximum Power Point Tracking (MPPT) control of a centralized TEG systems. • FDA-FNN Results are compared against PSO, BMO and GWO based MPPT Techniques. • 99.9% power efficiency and 6% more energy under dynaic operating conditions is achieved. • Varying stochastic temperature, varying load, Nicholas-Zigler trained PID and experimental validation cases are studied. [ABSTRACT FROM AUTHOR]

• Optimization model for the integrated assessment of the green ammonia value chain. • Case study for ammonia trade between Australia and Germany in 2030. • Sensitivity analysis on ammonia prices for technical and economic parameters. • Cost comparison of conventional and green ammonia production. This paper contributes to understanding the transformation of global energy trade to green energy carriers, focusing on green ammonia as the foreseeable first green hydrogen carrier. We provide a comprehensive overview of today's ammonia trade and assess scaling options for the trade of green ammonia. To that aim, we develop an optimization model for the integrated assessment of the green ammonia value chain that covers all steps from green ammonia production in an exporting country, up to delivery to a harbor in an importing country. The model endogenously chooses among different technology options and determines cost minimal operation. In a case study, we apply the model to the large-scale import of ammonia from Australia to Germany in a scenario for 2030. The results show that green ammonia can reach cost parity with gray ammonia even for moderate gas prices (but not necessarily with blue ammonia) if CO 2 prices are high enough. We also provide a sensitivity analysis with respect to the interest rate and other key technical and economic parameters and show that cracking ammonia to provide pure hydrogen comes at a 45 % cost markup per MWh at the destination. [ABSTRACT FROM AUTHOR]

With the economic development of various countries in the world, the traditional energy supply has been unable to meet the demand of current economic development, and the environmental problems caused by the use of traditional energy are also a major issue that needs to be urgently solved in all countries in the world, so the development and use of clean energy is an important measure to alleviate these two problems. This paper mainly studies the coupling mechanism and development prospect of the innovation ecosystem of clean energy under the smart agriculture based on blockchain. This paper first analyzes the coupling mechanism between the blockchain smart agriculture and the clean energy innovation ecosystem; secondly, it establishes the evaluation index system of the clean energy innovation ecosystem, and measures the development efficiency of the clean innovation ecosystem by using the SBM model, super efficiency SBM model and the global principal component method; thirdly, it uses the development efficiency of the clean energy innovation ecosystem through coupling and coordination The model calculates the coupling coordination of clean energy innovation ecosystem in different regions, and analyzes the spatial-temporal differences, regional differences and spatial agglomeration of the coupling coordination degree. The calculation results show that the average coupling coordination degree of green innovation development in the East, the middle, the West and the whole country is consistent with the development trend of provinces, autonomous regions and cities, showing an upward trend. The results show that the eastern region increases from 0.62 to 0.70; the average coupling coordination degree of the central region is lower than the national average coupling coordination degree, and the distance is gradually increasing; the western region has been in a serious maladjustment level, the global Moran index I value of the coupling coordination degree of clean energy innovation ecosystem is greater than 0, and the Z statistics are greater than the critical value of normal distribution function at the level of 5 %, P values are all less than 0.05 Local Moran index I and Z statistics have been growing steadily, and the significance of spatial positive correlation is getting higher and higher. The research results of this paper can provide a useful reference for the development of clean energy innovation ecosystem under the smart agriculture based on blockchain. [ABSTRACT FROM AUTHOR]

Paper-manufacturing industries utilize rice straw (RS) as raw material, generating huge quantity of black liquor (BL), which should be treated before reaching the environment. BL is mainly rich in organic pollutants that can be utilized to obtain valuable by-products and meet sustainable development goals (SDGs). In this investigation, BL from a paper-making industry was efficiently employed as a substrate for bio-H 2 generation via dark fermentation at pH value of 7.5 and temperature of 35 °C for 14 days, resulting in a hydrogen yield (HY) of 0.579 mol/mol glucose. The HY was substantially increased up to 1.654, 1.908, and 2.187 mol/mol glucose with immobilization of anaerobes on Graphene (GN), hydroxyapatite (HN), and Graphene/hydroxyapatite nanoparticles (GHN). Further, the activities of protein- and carbohydrate-degrading enzymes i.e. protease, α-amylase, xylanase, and CM-cellulase was highly improved. Results were validated with respect to the operational taxonomical unit (OUT) richness estimators (ACE and Chao), diversity indices (Shannon and Simpson), and electron-equivalent and mass balances. The H 2 productivity data were techno-economically assessed for proposing a bioenergy-based project, revealing a payback period of 5.92 y to recoup the initial investment. The findings of this study would promote interaction in the areas of industrialization, pollution prevention, and clean energy production to attain SDGs keys. [Display omitted] • Black liquor from paper manufacturing was used as a substrate for H 2 fermentation. • Graphene/hydroxyapatite nanoparticle supplementation improved H 2 yield by 278%. • OTU alpha diversity indices were enhanced with graphene/hydroxyapatite addition. • Electron-equivalent and COD mass balances assessed H 2 formation pathways. • Sustainable development goal numbers 6, 7, 8, 9, 13, and 14 were attained. [ABSTRACT FROM AUTHOR]

Is there a trade-off between spending on the green economy and an economy's strength? This paper addresses this question by estimating output multipliers for spending in clean energy and biodiversity conservation, and by comparing these to multipliers of spending on non-ecofriendly energy and land use activities. Using a new international dataset, we arrive at two key results. First, we find that every dollar spent on key carbon-neutral or carbon-sink activities can generate more than a dollar's worth of economic activity, whereas non-green spending returns less than a dollar. Second, for categories of spending where formal comparisons are possible, like renewable versus fossil fuel energy, we find that multipliers on green spending are about twice as large as their non-green counterparts. The point estimates of the multipliers are 1.1–1.7 for renewable energy investment and 0.4–0.7 for fossil fuel energy investment, depending on horizon and specification. These findings survive several robustness checks and lend support to bottom-up analyses that find that stabilizing climate and reversing biodiversity loss go hand in hand with economic prosperity. [ABSTRACT FROM AUTHOR]