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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 (CO2) and methane (CH4). 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. [ABSTRACT FROM AUTHOR]

Domestic regional synergistic emission reduction is important in achieving national climate goals. This study constructed a game theory-based model for regional synergistic emission reduction, modified the Basic Climate Game using the exact-hat algebra method, and expanded the game model using a general spatial equilibrium model to incorporate cross-regional economic impacts generated by emission reduction actions through factors and product flows. The formation of regional comparative advantages in emission reductions and their impact on synergistic emission reductions were revealed through regional characteristics such as emission elasticity, sectoral structure, regional trade shares, and green total factor productivity. A form of synergy was then proposed that utilizes the comparative advantages of different regions, allowing for synergistic emission reductions across different income regions and engagement with regions that are still at the carbon-peaking stage in cooperation. Moreover, the model was created to be as close to the economic reality as possible to provide a trade, industry, and economic growth policy that complements emission-reduction policies. [ABSTRACT FROM AUTHOR]

To measure and analyze the evolution characteristics and influencing factors of China's industrial green development level is of great significance in achieving carbon neutrality goal. Based on the panel data from 2000 to 2018 of 30 provinces in China, this research uses the super slack-based measuring model and the Malmquist-Luenberger index to calculates China's industrial green total factor productivity and to describe its evolution characteristics using the kernel density function and moreover uses the Spatial Durbin model and the partial differential method to explores its main influencing factors. This study finds that China's overall industrial green development level is not high but shows an upward trend year by year; carbon emissions, fiscal decentralization, and urbanization are not conducive to improving the national industrial green development level, whereas economic development, foreign direct investment, industrial structure, and technological progress are positive contributors. At the same time, the level of economic development and technological progress have significant direct and spatial spillover effect. Our findings also provide some policy implications for improving China's industrial green development. [ABSTRACT FROM AUTHOR]

China's energy-intensive industry (EII) is characterized by high pollution, high energy consumption, and high emissions. It is essential to boost this sector's green total factor productivity (GTFP) in order to support the sustainable development of the China's economy and help to achieve the objective of carbon neutrality. This work measures the evolution of GTFP in EII and its subsectors at provincial and regional level from 2001 to 2019, identifies the causes of these changes, and finally analyzes the particular spatial aggregation effect of GTFP in EII. It is discovered that the GTFP of China's EII has significantly improved throughout the sample period and exhibits a spatial structure of "high in the coastal areas and low in the west and center." The main driver of GTFP growth for China's EII and its subsectors was technological advance. Smelting and pressing of ferrous metals (SPFM) and smelting and pressing of non-ferrous metals (SPNM) were the industries with the most significant technological progress. Remarkable spatial correlations existed among the GTFP of EII at provincial level. The GTFP values of EII in coastal regions were relatively high and tend to benefit the adjacent provinces but there was a polarization effect in the Middle Reaches of Yellow River (YR). Finally, policy implications are provided for the sustainable development of China's EII. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality and improve emission reduction efficiency, capturing carbon dioxide from the air on a large scale and promoting the application and innovation of carbon capture technology (CCUS) are the most important goals. This study undertakes an annual and comprehensive evaluation of the policy and the technological innovation efficiency (TIE) of 10 listed companies in China using the DEA model and the Malmquist index analysis method. The number of relevant laws and policies is significant, but they are not well coordinated. The static evaluation results indicate that the complete factor production rate is low, generally lower than 0.9, and the technical innovation efficiency is weak, mainly because of technological backwardness. The dynamic evaluation results indicate that the changes in total factor productivity (TFP) each year are primarily affected by changes in technological progress. This suggests that most domestic enterprises are still exploring technological innovation (TI) and operational business models. Finally, this study proposes measures to improve the TIE of carbon capture technology enterprises in China, including giving full play to the role of the government, expanding effective investment, and improving innovational ability. [ABSTRACT FROM AUTHOR]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China's carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020–2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56–0.57% in 2020–2060; this is hard to achieve. In addition, the study finds that China's 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a significant effect. Therefore, the paper believes that the key to carbon neutrality lies in the coexistence of technological innovation and carbon pricing to ensure that we can cope with global warming with the lowest cost and resistance. [ABSTRACT FROM AUTHOR]

To examine the intricate relationship between digitalization development and sustainable development, this study commences its analysis by employing the entropy method to establish an integrated indicator of digitalization development among advanced nations. Subsequently, we utilize a panel dataset spanning a decade from 2010 to 2020, encompassing 36 developed countries, to estimate Green Total Factor Productivity (GTFP) growth. We establish a fixed effects model, a mediation effect model and a threshold regression model to explore the relationship between the two variables. The empirical findings of this research reveal a significant positive association between the progress and enhancement of digitalization and the advancement of sustainable development (GTFP). Furthermore, this research elucidates the underlying mechanism by which digitalization development indirectly fosters the advancement of green productivity. This facilitation occurs through the promotion of technological innovation, the enhancement of human capital, and tupgrades in the industrial framework. Notably, our study finds the non-linear relation between digitalization development and GTFP growth, suggesting that the magnitude of impact on environmental performance is contingent on the scale of technological innovation, the growth of human capital, and the level of financial development. • The GTFP and digitalization among OECD countries are investigated; • Digitalization has direct effect on GTFP in OECD countries; • Digitalization has indirect effects on GTFP in OECD countries; • There is nonlinear relationship between GTFP and digitalization. [ABSTRACT FROM AUTHOR]

The present trend of rapid expansion in the textile industry is highly polluting and energy intensive, and the government intervention is necessitated. This study develops a Data Envelopment Analysis (DEA) model, examines the green technology innovation of listed textile enterprises, and explores its determining factors in order to address the issue of carbon emission reduction in the textile sector. The research sample for this study is obtained from 42 listed textile enterprises from 2017 to 2020. The research demonstrates: (1) the green technology innovation efficiency of domestic listed textile enterprises remains marginal; (2) from 2017 to 2020, the mean value of total factor productivity (TFP) and its decomposition index of listed enterprises in China's textile industry exhibit a downward trend, primarily as a consequence of the slowdown of technological progress; (3) the green technology of the entire industry is in recession from 2017 to 2020; (4) green technology innovation efficiency is inversely correlated with government subsidies, but positively correlated with net profit and asset size of enterprises. In light of the above information, enterprises should boost their ability to innovate with green technologies and implement a research and development (R&D) management development strategy that gives equal consideration to both economic and environmental benefits. [ABSTRACT FROM AUTHOR]