Towards Carbon Neutrality: The Impact of Renewable Energy Development on Carbon Emission Efficiency

Incentive-compatible mechanism for manufacturing carbon emission supervision under carbon control policies in China

Enhance performance in manufacturing carbon emission (MCE) reduction has become a widespread consensus and a necessary part, which cannot be achieved without the joint participation of manufacturing enterprises and supervisory departments. Accordingly, how to coordinate the interests of both sides and design a reasonable incentive-compatible mechanism becomes an urgent task at present. Considering the two subsidy funding channels of peer funds and government finance, this study applies the evolutionary game model to analyze feasible schemes for designing incentive-compatible mechanism of MCE supervision, discusses and simulates the realistic scenarios and influencing factors of incentive-compatible mechanism under the non-subsidized and subsidized schemes. The results show that MCE supervision is in an incentive-incompatible state under the non-subsidized incentive scheme, while in a constrained incentive-compatible state under the subsidized incentive scheme. With the increase of peer funds and penalty coefficient or the decrease of subsidy coefficient, the period of MCE supervision to reach an incentive-compatible state becomes shorter. However, a lower peer fund and penalty coefficient or a higher subsidy coefficient will contribute to a state of incentive-incompatible or a periodic cycle state of "incentive-compatible → incentive-incompatible →incentive-compatible→…" in the MCE supervision.

Assessing the impact of urban planning policies on renewable energy: A case a China using the DID estimation model

The ongoing pace of urbanization poses a substantial obstacle to the concurrent progress of both financial and ecological development. Recognizing this challenge, governments globally are formulating cutting-edge strategies for urban renewal to ensure the long-term sustainability of cities. In this context, we employ a difference-in-differences model to scrutinize the intricate relationship between smart cities and the growth of renewable energy, utilizing the Chinese smart city pilot program as a pertinent experiment. This analytical approach provides novel insights into the underlying reasons behind this correlation. The research yields three noteworthy findings. Firstly, it underscores the indispensable role of pilot initiatives in smart cities for advancing the cause of renewable energy. Secondly, the study reveals a positive and beneficial interplay between creativity, economic inclusion, and the utilization of technological innovation in experimental urban programs, suggesting a potential multiplier effect. Thirdly, the local context significantly influences the impact of smart city pilots, with the dissemination of renewable energy being particularly effective in resource-rich, metropolitan, and coastal cities. Observable impacts of current smart city experiment on energy security and sustainable development are already apparent. The research findings contribute fresh perspectives to the complex challenges of sustainable energy production and urban planning, especially in developing countries like China.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

The mechanism of China's renewable energy utilization impact on carbon emission intensity: Evidence from the perspective of intermediary transmission

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies.

Evaluation and spatial convergence of carbon emission reduction efficiency in China's power industry: Based on a three-stage DEA model with game cross-efficiency

Reducing carbon emissions is essential for achieving sustainable development in China. In this study, we developed a framework for measuring carbon emission reduction considering both thermal power and clean energy generation perspectives. Subsequently, we constructed a three-stage data envelopment analysis (DEA) model with game cross-efficiency to eliminate the influence of external environmental factors, random factors, and regional competition. Thereafter, we calculated the carbon emission reduction efficiencies (CEREs) of the power industries of 30 provinces in China from 2010 to 2020. Based on this, we conducted temporal and spatial analyses of the carbon emission reduction amount (CERA), evaluated CERE, compared different DEA models, and assessed spatial convergence effects. The evaluation results of CERE across 30 provinces, cities, and autonomous regions in China showed that: (1) although China's CERA increased, substantial regional differences exist in the carbon emission reduction structure. (2) The overall average CERE ranged from 0.485 to 0.737. Since 2016, the highest CERE values have been observed in southwest China, followed by the eastern coastal and central regions, while the northwestern region experienced notable fluctuations. (3) The three-stage DEA model with game cross-efficiency, which eliminates the influence of competition and external environmental factors, can be used to accurately measure CERE. (4) CERE has a spatial convergence effect on China's power industry and is promoted by energy structure, upgrading of industrial structure and government interventions. These findings provide important insights for optimizing the carbon emissions reduction structure and improving CERE.

The determinants of carbon emissions in Belt and Road Initiative countries: analyzing the interactive role of information and communication technologies

Belt and Road Initiative (BRI) countries have benefited greatly from the intelligent growth of the green economy made possible by the widespread adoption of internet and mobile phone technologies. In addition, renewable energy consumption endorses sustainable development. Therefore, the purpose of this research is to determine if the use of information and communication technology (ICT) and renewable energy consumption has an effect on sustainable development in BRI countries, while using the augmented mean group (AMG) model, AMG robustness test, and panel Dumitrescu-Hurlin causality test to get robust results. According to the results of the study, the information and communication technology, renewable consumption, human capital, and urbanization reduces the emission of carbon dioxide emission in BRI countries while economic growth enhances the CO2 emission. Therefore, it is recommended that BRI countries increase their inter-regional cooperation in order to boost investment in renewable energy, effectively use the spillover effect of technology and knowledge, and end the resource curse in environmental policy. Based on the results, the authors of this paper propose a number of important steps toward environmental sustainability.

Analysis of carbon emission drivers and multi-scenario projection of carbon peaks in the Yellow River Basin

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China's high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005-2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios "M-L", "M-M" and "M-H" will occur in 2030 at the earliest. The "M-H" scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path.

How natural resources depletion, technological innovation, and globalization impact the environmental degradation in East and South Asian regions

Rapid economic expansion has caused resource depletion, globalization issues, and environmental deterioration. Globalization has highlighted East and South Asian mineral richness. This article investigates the effects of technological innovation (TI), natural resources, globalization, and renewable energy consumption (REC) on environmental deterioration in the East and South Asian region from 1990 to 2021. The cross-sectional autoregressive distributed lag (CS-ARDL) estimator is used to estimate short- and long-run slope parameters and dependencies across countries. The results demonstrate that many natural resources significantly enhance environmental degradation, while globalization, TI, and REC reduce emission levels in East and South Asian economies and that economic growth significantly degrades ecological quality. This research suggests that governments in the East and South Asian region develop suitable policies that promote the efficient use of natural resources via technological advancements. Furthermore, future policies regarding energy consumption, globalization, and economic development should be aligned with the aims of sustainable environmental development.

Quantile VAR network evidence for spillover effects and connectivity between China's stock markets, green commodities, and Bitcoin

Numerous economic and financial crises, particularly the present crisis in the healthcare sector, have pushed major shock spillover channels over stock marketplaces. This research studied how the shock spillover system is affected by three significant factors: Bitcoins, unpredictability, and the China stock market between 2014 and 2021. While much earlier empirical research has looked at risk dispersion in different financial markets, this article will zero in on green markets. This investigation seeks to accomplish something that has never been done before: determine whether or not green commodities, Bitcoin, and uncertainty impact the performance of the China stock market. The following are significant results based on a quantile vector autoregressive (VAR) connection. (i) A static spillover system indicates that information was widely shared across markets during intense market circumstances. (ii) The global green economy and clean energy marketplaces are the primary sources of knowledge spillover in adverse market conditions. This research elucidates the asymmetrical influence of green products, Bitcoin, and market volatility in China. This is vital due to the dynamic nature of international and regional connections. Recent studies have shown that shock spillovers are excellent for cryptocurrencies such as Bitcoin (BTC), uncertainty indices, and global carbon indexes, but bad for most eco-friendly products.

Simulation study on the development decision of new energy manufacturers under the weight of consumption responsibility

In 2019, China proposed the weight of consumption responsibility (WCR) policy for renewable energy. This study first analyzes the interaction between the electricity market and the green certificate market and establishes a two-market equilibrium model with multiple subjects. Secondly, a system dynamics model of the development decision of the new energy manufacturers (NEMs) under the interaction of the two markets is established. Finally, taking Henan province as an example, the development trend of NEMs is simulated, and sensitivity analysis is conducted to explore the key influencing factors for the development of NEMs. The results show that the electricity market and the green certificate market interact with each other mainly through the price of electricity and the price of green certificates, thus evolving the trading behavior of trading subjects. By 2035, for every 0.25% increase in non-hydropower WCR in Henan province, the annual added new energy installation will be around 1200 MW. In the current scenario, the policy of certificate multiplier will be detrimental to the overall development of NEMs, and a conservative investment strategy and accelerated technology improvements are more beneficial to NEMs. Carbon emission peaking and carbon neutrality policies can also accelerate the growth of new energy installations.

The Dynamic Evolution of Global Energy Security and Geopolitical Games: 1995~2019

Under the influence of economic globalization, the internationalization trend of energy security issues has become increasingly prominent. This paper adopts the natural discontinuity grading method to classify the energy security status of 102 countries into five categories: Best, Better, Good, Poor and Worse types; reveals the dynamic evolution characteristics and main formation mechanisms of world energy security; and puts forward the game focus of future energy geopolitics. The results show that: (1) during 1995-2019, global energy security presents local turbulence and an overall "J" shaped trend; (2) the global energy security pattern coincides with the international geopolitical order. The countries with the "Best" energy security are found in Western Europe and North America while the countries with "Poor" or "Worse" energy security are located in Asia and the less developed regions of Africa; (3) the main reason why developed economies have better energy security is due to their high energy use efficiency, while developing countries lag behind mainly because of their lower innovation capacity, lower productivity and lower disposable income; and (4) the global energy security landscape is expected to be affected by the changing US-China relationship, coercive energy transition and the uncertainty of the political environment.

Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities

Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.