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The transition toward carbon neutrality in China necessitates integrating more renewable energy sources (RES) into the power grid. However, a high share of RES can destabilize the grid, making it crucial to add clean, flexible power sources, such as battery energy storage systems (BESS) and flexible coal power combined with carbon capture and storage (CCS). This study shifted focus from traditional power plants to flexible power solutions, including BESS and CCS, and adopted a macro perspective. An hourly basis simulation model of the power system was developed to assess the cost-effectiveness of these flexible power options. The results revealed that BESS is more cost-effective when RES penetration rates are low, whereas CCS becomes more advantageous as RES constitutes a larger portion of the power supply. Combining BESS and CCS can ensure grid stability and maximize RES utilization. Sensitivity analysis indicated the need to control power demand growth to support this transition effectively.

Extensive research has discussed digital inclusive finance’s (DIF) rapid growth in financial services and its vital economic role; however, limited research addresses its potential to mitigate micro, small- and medium-sized enterprise (MSME) financing constraints and generate environmental benefits. This study empirically investigates the effects of DIF on Chinese MSMEs’ environmental performance using a substantial manufacturer sample. The findings indicate that DIF significantly reduces the wastewater discharge intensity of MSMEs. Furthermore, DIF has a more pronounced environmental effect on non-state-owned firms, SMEs, firms in areas with more significant emission reduction pressure, and those in the eastern region. Further mechanism analysis reveals that DIF can ease the financing constraints of MSMEs. Moreover, strengthening terminal treatment, optimizing the energy consumption structure, and increasing investment in research and development are significant pathways through which DIF affects corporate environmental performance. Finally, this study proposes some policy implications to promote the green transformation of MSMEs.

The Conference of the Parties (COP26 and 27) placed significant emphasis on climate financing policies with the objective of achieving net zero emissions and carbon neutrality. However, studies on the implementation of this policy proposition are limited. To address this gap in the literature, this study employs machine learning techniques, specifically natural language processing (NLP), to examine 77 climate bond (CB) policies from 32 countries within the context of climate financing. The findings indicate that “sustainability” and “carbon emissions control” are the most outlined policy objectives in these CB policies. Additionally, the study highlights that most CB funds are invested toward energy projects (i.e., renewable, clean, and efficient initiatives). However, there has been a notable shift in the allocation of CB funds from climate-friendly energy projects to the construction sector between 2015 and 2019. This shift raises concerns about the potential redirection of funds from climate-focused investments to the real estate industry, potentially leading to the greenwashing of climate funds. Furthermore, policy sentiment analysis revealed that a minority of policies hold skeptical views on climate change, which may negatively influence climate actions. Thus, the findings highlight that the effective implementation of CB policies depends on policy goals, objectives, and sentiments. Finally, this study contributes to the literature by employing NLP techniques to understand policy sentiments in climate financing.

Abstract Global shocks potentially distort economy’s achieved equilibria. Considering the 2020 global crude oil price shock and the 2019 coronavirus disease pandemic, this study proposes an energy and environment integrated general equilibrium model to analyze the economic, energy, and environmental effects of these global shocks on Nigeria, a developing, oil-producing, oil-dependent, and oil-exporting country. Furthermore, the mitigating roles of a fiscal stimulus–response package (palliative) are investigated and analyzed. Generally, the developed model predicts a decline in the level of economic activities. The study results are unsurprising due to Nigeria’s heavy reliance on crude oil. However, sectorial-specific impacts exist as some sectors experience output declines while others do not. Environmental quality is improved since more carbon is abated, nonetheless. Carbon intensities increased given that the price effects outweighed the quantity effects— reduced emission results from reduced economic activities and not from technological progress. The results further show a revenue-abatement paradox; a fixed carbon tax approach minimizes the tax revenue loss but may discourage carbon abatement. Conversely, the ad valorem and specific carbon tax systems encourage carbon abatement but reduce carbon tax revenues. The government’s fiscal policy stimulus–response (palliative) action dampens the impact of these global shocks on both the domestic agents and the overall economy. The results are robust and can be applied to the experiences of other developing oil-producing, oil-exporting, and oil-dependent economies.

Some research on climate change has been the basis of climate change denialism (hereafter, CCD is used to refer to denial, denialism, and deniers). There is formative knowledge about the role of political propaganda in climate policies and resulting outcomes. To contribute to the understanding of political ideology and the extent of CCD, we adopt econometric techniques to study the impact of the United States of America's withdrawal from the Paris Agreement. The results show that ideological political propaganda has psychological and philosophical consequences that impact climate change policies. In addition, we find that the effect of a state's economic growth on climate change is mediated by CCD. In particular, low-income capitalist states have a higher propensity to become CCDs because they are more likely to engage in economic expansion even at the expense of environmental sustainability. Global climate change policies depend on high-income nations and industries' willingness to adopt economic policies to achieve sustainable future development. Thus, this study fills the literature gap on the relationship between political ideology and climate change. The findings show that CCD significantly influences voting patterns and socioeconomic outcomes. It impedes states from achieving net-zero emissions and carbon neutrality and it is used as a political propaganda. Subject to these findings, relevant policy suggestions are offered.

Abstract Existing studies mostly discussed the impact of transportation infrastructure on the economy and society. However, the environmental performance of digital infrastructure has been discussed less. This study explores the effect of digital infrastructure construction on green economic transformation based on theoretical analysis. Using the Broadband China policy as a quasi-natural experiment, the authors construct a staggered difference-in-difference (DID) model and empirically assess the effect of digital infrastructure on green economic development with panel data of Chinese 271 cities from 2003 to 2019. First, the main results indicate that digital infrastructure can enable green economic performance in Chinese cities. The results remain robust after considering the heterogeneous treatment effects, placebo test, and excluding the effect of other policies. Second, the heterogeneity results indicate that green economic performance in eastern and economically developed cities benefits more from digital infrastructure construction. Finally, by enhancing energy efficiency, fostering digital industrialization, and stimulating green technology innovation, the digital infrastructure indirectly contributes to the urban green economy development. This study put forward some constructive policy suggestions to promote green economic transformation from the digital infrastructure construction perspective.

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

Abstract This study addresses a crucial gap in the existing literature by exploring the intricate relationship between gender, disability, and energy poverty. While prior research has shown that females and persons with disabilities are more vulnerable to energy poverty, our study adopts an intersectionality framework to investigate how these identities interact with other variables, including life dissatisfaction, food insecurity, and energy subsidy, to shape the experience of energy deprivation. Using a series of robust techniques, our analysis of the General Household Survey in South Africa reveals several noteworthy findings. First, while females are less likely to be energy poor, the intersection between females and disability significantly amplifies their risk of energy poverty by 2.6%. Our mediation analysis further elucidates that life dissatisfaction and food insecurity serve as critical mechanisms through which this intersection exacerbates energy poverty. Importantly, we also find that the impact of energy subsidy is most effective when targeted toward females with disabilities, highlighting the need for tailored interventions. We call for policymakers and stakeholders to prioritize targeted energy subsidy schemes for persons with disabilities and females, recognizing the critical role such policies can play in mitigating energy poverty and promoting equity.

Abstract Guiding individuals to adopt pro-environmental behaviors is critical to achieving carbon neutrality goals. Public policy targeted at a small number of people may be amplified by interpersonal interaction, making peer effect a potentially useful tool for accelerating problem solving. However, previous studies have paid insufficient attention to the influence of peers on university students’ energy-saving behaviors. This paper attempts to examine the effect using a large-scale survey dataset conducted on students from Xiamen University in China and the classical linear-in-means model. The result shows that peers’ pro-environmental behaviors have positive effect on individuals. The heterogeneity of the influence and the potential mechanisms are also explored. This paper contributes to very important and still growing literature dealing with contagion processes in pro-environmental behaviors. The policy implication is that the government should make full use of the peer effect to maximize the benefits of energy-conservation campaigns.

Pakistan is moving toward the large-scale use of coal. Coal plays a dominant role in Pakistan’s energy mix and is estimated to reach 30% by 2030. The purpose of this study is to analyze coal imports and indigenous reserves in relation to CO2 emissions. In particular, this study constructs the logarithmic mean Divisia index (LMDI) method to see the impact of the factors, decoupling index for the economic relationship, and pollution from coal-fired power plants from 1986 to 2019. The empirical results show that 1) coal consumption and imports are interrelated, while coal production has had the lowest production level since 1986; 2) the energy intensity impact plays a medium role in decreasing coal utilization, followed by the coal share effect; however, the aggregated impact accounts for 0.023% of the total coal use; 3) the economic and population activity effects progressively increase with coal consumption by 0.25% and 0.35%, respectively, with the annual average growth; 4) only “three” decoupling states were observed: expansive coupling, expansive negative decoupling, and weak decoupling. Expansive negative decoupling occurred due to high energy share and energy intensity. Expansive coupling occurred only in 2001 due to rapid growth in coal proportion and a sluggish decrease in energy intensity, and weak decoupling showed a decoupling association between economic growth and coal utilization; and 5) the various coal compositions, such as moisture, volatile matter, fixed carbon, ash, and sulfur, can be evaded by 1.82, 4.83, 5.16, 1.43, and 0.39 Mt currently. Finally, environmental analysis recognized that implementing clean coal technologies significantly saves fuel and, consequently, reduces emissions. This study also discusses further policies.

Based on the two-tier stochastic frontier analysis method, this paper constructs a basic model to analyze the environmental treatment investment of enterprises, and discusses the influence of the gambling power between enterprises and governments on the input of environmental pollution control. The data in this paper are from the Chinese Private Enterprise Survey (CPES). And empirical results show that the gambling power between enterprises and government affects the investment of pollution control to a great extent. The influence proportion of the gambling power of the enterprise on the investment of environmental pollution control is 84.44%, which is much higher than that of the government (15.56%). The enterprise is the executor with more real information, and can even reduce the investment of pollution control by concealing its actual situation and so on, hence the enterprise occupies the initiative in the game process. Although enterprises are still in the dominant position in the game, their advantages are continually shrinking. With the expansion of the scale of the enterprise and the high level of entrepreneur education, the game advantage of the enterprise is continuously decreasing.

This paper presents a time-of-use (TOU) pricing model of the electricity market that can capture the interaction between power plants, generation ramping, storage devices, electric vehicle loading, and electricity prices. Even though the model is calibrated to Chinese Fujian electricity market, it can be used to predict efficient equilibrium tariffs on any electricity system that seeks to emphasize the marginal costs principle for generators where consumer tariffs are regulated. We show that, for efficiency purposes, it is imperative for dynamic electricity pricing models to take into account technology-specific ramping parameters and various sources of power system flexibility. Also, by utilizing unused battery capacity, electric vehicles can significantly complement other storage devices to reduce power sector emissions and ensure grid stability. The model and results presented in this paper are useful to regulatory bodies in Fujian province for economic policy, and to the scientific community in terms of (i) offering a tool to promote more discussion on dynamic pricing in electricity markets, and (ii) enriching our understanding of how to design a TOU program in the light of flexible power resources.

Carbon emission is inevitable, and changes with energy consumption and economic development, presents policy options toward sustainable development path. Currently, there is little assurance from policymakers in committing to climate change pledges: taking the Middle-East and North African (MENA) region as a specific case with using 2019 as a cut-off period. We conducted an interim assessment of the Paris Agreement to ascertain whether climate actions are in tandem with emissions reduction targets. Making use of difference-in-difference technique as a quasi-experiment supported by fixed-effects and placebo treatment models, the results point to evidence of less than 1% effective CO2 emissions reduction as of 2019 compared to the 2015 level. The current carbon emissions reduction commitment level is far-reaching contrary to the Nationally Determined Contributions (NDCs) pledged (16.43% on average by 2030 compared to the 2015 level). The analysis suggests that achieving NDCs commitments are currently major burden on policymakers since the economic development is highly linked with nonrenewable energy consumption. Furthermore, a more comprehensive framework when accounting for all available renewable and clean energy projects shows reduction levels in the range of 30%–40% from 2020 to 2030. These results suggest that the Paris Agreement in MENA countries may be more effective from 2020, thus underscore the importance of capturing ongoing projects (involving renewable and clean energy technologies) into interim policy assessment. The shared implication is that greater efforts are demanded from the region and at country levels to further decrease emissions through the use of negative emissions technologies particularly in the electricity generation sector.

Abstract Carbon tax and some other policies are designed to reduce emissions; resource tax can raise the energy price from the supply side to achieve the purpose of emission mitigation. Based on previous studies, this paper abstracts mitigation policies into supply-control (resource tax as an example) and demand-control (carbon tax as an example). The effects of these policies have been divided into the direct and the indirect effects. A dynamic recursive computable general equilibrium model is applied to simulate different impact path of the two policies. The research shows that if there is no foreign trade and the market is completely market-oriented, the effect of the demand control and the supply control may be equivalent. But this is not the real case. Under the same level of CO2 emission, carbon tax can significantly reduce the energy demand of enterprises and restrain energy imports. However, resource tax can significantly increase domestic energy prices firstly, and then enterprises will be more willing to use cheaper imported energy. Regardless of energy security, relatively low energy use costs ease the economic costs of emission mitigation. Therefore, if every country in the world is required to reduce emissions compulsorily, resource tax may be a better policy of reducing emissions while obtaining “excess profits”.

Renewable energy technology innovation (RETI) and green industrial structure upgrading (GISU) are crucial pathways for China’s pursuit of sustainable development. Despite the existing research separately emphasizing the importance of RETI and GISU, there are limited investigations into the role of RETI in facilitating the process of GISU, especially in terms of empirical evidence at the urban level. This study utilizes data from 284 cities in China from 2006 to 2020 to investigate the impact of RETI on GISU. Our research findings reveal a positive association between RETI and GISU. Specifically, increasing 1 unit of RETI can promote GISU by 0.19 units. Moreover, we identify that environmental regulations, green finance, and human capital strengthen RETI’s stimulating effect on GISU. Notably, the study highlights that the positive influence of RETI on GISU is especially prominent in economically developed cities with a conducive innovation environment and those facing severe pollution challenges.

India is often referred to as the next development superpower, and generally, becoming a large-scale industrialization center is seen as an achievable goal for the country. This article investigates the output elasticity, substitution elasticity, and technological advancement between the various factors (i.e., labor, capital, and energy use) in the industrial sector of India. To investigate the factor's productivity, a trans-log production function was applied; however, ridge regression was used to analyze the various parameters to check the multicollinearity issue. The results show that (1) the analyzed inputs are optimistic and return-to-scale averages of 1.18, 1.41, and 1.24 between labor, capital, and energy, respectively, are increasing; (2) the pairs substitution between labor--industrial energy utilization and capital--industrial energy consumption is found to be 0.96 and 0.98, respectively, on average, indicating that capital, labor, and energy are good substitutes that need more attention in the production process; and (3) the technological progress between factors ranges from -0.4 to 0.02, in which labor--energy and capital--energy utilizations provide quicker outcomes than a capital--labor utilization. Finally, the industrial sector can attain maximum productivity if capital and skilled labor are improved under the sustainable development goals, as energy and capital are optimized for maximum efficiency. Finally, energy substitution and low-carbon technological efforts can be better suited for attaining dual-carbon goals in the industrial sector. [ABSTRACT FROM AUTHOR]

In the context of the "dual carbon" strategy, how to leverage green finance to promote China's wind power industry is a hot topic. Unlike existing literature, this article uses a nonparametric additive model to investigate the impact and mechanism of green finance on wind power development. Research has found that green finance has an inverted U-shaped nonlinear impact on wind power development, indicating that green finance has a more prominent contribution to the wind power industry in the early stages. Further mechanism research indicates that green finance affects the wind power industry through foreign direct investment and green technology innovation. Specifically, with the relaxation of foreign direct investment conditions in the energy sector, the role of foreign direct investment in promoting the wind power industrymore prominent in the later stages. In the early stages, government support was greater, and green technology patents grew rapidly, driving green technology innovation to have a more significant impact on the wind power industry. In addition, the impact of fiscal decentralization, wind power prices, and environmental regulations on the wind power industry also exhibits significant nonlinear characteristics. This article helps to comprehensively understand themechanismand impact of green finance on wind power development, and provides a reliable basis for optimizing green finance policy and effectively promoting wind power. [ABSTRACT FROM AUTHOR]

This paper constructs a comprehensive electricity market model in the context of China, highlighting the deviation caused by neglecting start-up costs from an engineering perspective. The model allows for the abandonment of excess wind and solar power generation, contributing to the achievement of research objectives in scenarios with a high proportion of renewable energy. Our method innovatively integrates fuel and carbon prices, clean energy expansion, and power system marginal prices according to the carbon trading rules of the Chinese power industry, providing a more accurate representation of market dynamics. Findings reveal that neglecting start-up costs can lead to significant biases in electricity prices. We demonstrate that the marginal price sometimes deviates fromthe fluctuation of the real value. While fuel and CO2 prices can be transmitted downstream, the value of new energy must be transmitted through its impact on the marginal unit. This insight is crucial for understanding the "missing money" problem in electricity markets. Based on these findings, we propose policy recommendations. We suggest considering fixed and average costs as pricing benchmarks and utilizing capacity utilization as a signal for demand response to adjust power pricing. Furthermore, we recommend trading different energy types separately in the spot market with different pricing benchmarks to ensure the homogeneity of marginal units. [ABSTRACT FROM AUTHOR]