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Energy demand and carbon emission in building construction and utilization have presented an increasing trend with urbanization development and improvement of living standard. Improving the carbon emission efficiency of the construction industry is a precondition for allocating carbon emission reduction objectives and interregional experience exchange over carbon emission reduction. An inputoutput index system was first constructed in this study to obtain the carbon emission efficiency of the construction industry. Carbon dioxide emission of the construction industry was taken as unexpected output, and the super efficiency slack-based measure-data envelopment analysis (SBM-DEA) model was used to estimate the carbon emission efficiency of the construction industry in Henan Province, China during 2008-2019. Finally, treatment measures were proposed to retard the rising trend of carbon emission in the construction industry. Results showed that the mean carbon emission efficiency of the construction industry in Henan Province during 2008-2019 is 1.007 and the carbon emission efficiency of the construction industry reaches the minimum value of only 0.807 in 2012. The carbon emission efficiency of the construction industry in Henan Province demonstrated an overall rising trend with a small amplitude during the investigation period. The results of this study can provide a reference for exploring the efficiency of the construction industry in Henan Province under carbon emission constraint, analyzing and identifying efficiency improvement objectives and methods for the construction industry, and facilitating its sustainable development.

With the introduction of national carbon neutrality targets, carbon emission reduction actions in developed countries have become a hot topic as part of the international community’s drive to take action to mitigate climate change. Carbon emission efficiency is an important indicator that can be used to measure progress toward carbon emission reduction targets. The relationship between green technology innovation and carbon emission efficiency has not been adequately studied, and the transmission mechanism is not yet clear. Based on the above research gaps, taking 32 developed countries that have proposed carbon neutral targets as research samples, this paper used spatial econometric models to explore the impact of green technology innovation on carbon emission efficiency and adopted spatial mediation model and spatial moderation model to analyze the transmission effects of economic development, urbanization, and financial development on environment-related green technology and carbon emission efficiency. This paper aimed to provide a policy basis for developed countries to mitigate carbon emissions and achieve carbon neutrality goals as soon as possible. The following results were obtained: (1) Luxembourg, Norway, and Switzerland were found to be efficient in terms of carbon emissions, while most developed countries were in an inefficient state. (2) Environment-related green technology innovation significantly improved carbon emission efficiency. (3) Economic development and urbanization had a mediating role on green technology innovation and carbon emission efficiency. In other words, green technology innovation could have an indirect impact on carbon emission efficiency by influencing economic development and urbanization. (4) Financial development could positively moderate the sensitivity of carbon emission efficiency to green technology innovation. Improving the level of green technology innovation is one way to improve carbon emission efficiency, and the mediating effect of economic development and urbanization can be used as a focus point to improve carbon emission efficiency. The pressure of carbon emission reduction can be moderated by finance development. The results of this study provide theoretical support that will assist developed countries in achieving their carbon neutrality targets.

This article discusses the influence and mechanism of green finance on carbon emission efficiency. Based on the panel data of 27 provinces and municipality in China from 2008 to 2017, the slack-based model of unexpected output is used to measure the efficiency of carbon emissions. On this basis, the Tobit model is used to empirically study the impact and mechanism of green finance on the efficiency of carbon emissions. The consequences exhibit that (1) China’s carbon emission efficiency is not high and generally presents a gradient decreasing characteristic of east, middle, and west. (2) Overall, green finance plays a considerable role in improving carbon emission efficiency; by region and group, there are significant differences in the influence of green finance on carbon emission efficiency. (3) The study found that green finance promotes the efficiency of carbon emission through technological progress and industrial structure upgrading. This study provides empirical evidence and policy enlightenment for the realization of carbon peaking and carbon neutrality goals and the evolution of green finance.

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

The Yellow River basin (YRB) is China’s most critical energy consumption and coal production area. The improvement of carbon emission reduction efficiency in this area is the key for the Chinese government to achieve the 2030 carbon peak and 2060 carbon neutral (“30.60”). Given this, this study first calculates the carbon emission efficiency of YRB from 2005 to 2019 based on the slack-based measured directional distance function (SBM-DDF) model and combined with Malmquist–Luenberger (ML) index and decomposes the carbon emission efficiency of each province. Then, a panel Tobit model with random effect is constructed to measure the influencing factors and their influence degree of carbon emission efficiency of YRB. Finally, the main influencing factors are selected, and policy suggestions on how to improve the carbon emission efficiency of each province are put forward with the help of the coupling coordination degree (CCD) model. The results show that first, the carbon emission efficiency of each province is significantly different, but it shows a fluctuating upward trend on the whole. Second, the reasons for the rise or decline of the ML index in different provinces are different. Therefore, the development strategies of different provinces should be formulated from the perspective of accelerating technological progress and improving technical efficiency. Finally, the calculation results of influencing factors and coupling coordination degrees show that provinces with high coupling coordination degrees should focus on developing per capita power consumption and controlling per capita power consumption to consolidate the actual urbanization process and industrial structure adjustment. Provinces with low coupling coordination degrees should focus on maintaining the urbanization process and increasing the development of the tertiary industry. Therefore, to fundamentally reduce carbon emissions in YRB areas, we need to consider implementing differentiated emission reduction schemes based on national strategic objectives and in combination with the development characteristics of various provinces.

The energy transition and carbon emission efficiency are important thrust and target functions, respectively, for achieving carbon neutrality in the future. Using a sample of 30 Chinese provinces from 2006 to 2018, we measured their carbon efficiency using the game cross-efficiency data envelopment analysis (DEA). Then, a random forest regression model was used to explore the impact of renewable energy development on regional carbon emission efficiency. The results are as follows. First, China’s carbon emission efficiency in the southeast coastal area was better than that in the northwest area. Second, renewable energy development first inhibited and then promoted carbon emission efficiency, and there existed a reasonable range. Third, through a regional heterogeneity analysis, the trend of the influence of renewable energy development on carbon emission efficiency was found to not be significantly different in eastern, central, and western China, but there was a certain gap in the reasonable range. Our study not only helps to promote the study of renewable energy development and the carbon neutral target, but also provides an important reference for Chinese policy-makers to design a reasonable carbon emissions reduction path.

Carbon emission efficiency directly determines the level of green economic development. Based on the panel data of China’s Yangtze River Economic Belt (YEB) from 2008 to 2017, this paper uses the stochastic frontier analysis (SFA) model to analyze the overall carbon emission efficiency level, influencing factors, and changing trends, with a view to discussing the relationship between economic development and carbon emission efficiency. The results suggest, first, the overall carbon emission efficiency of the YEB is on an upward trend, but there is still much room for improvement. Second, the impact of industrialization and urbanization on carbon emission efficiency follows a U-shaped. As industrialization and urbanization progress, the impact on carbon emission efficiency shows a downward and then upward trend. Third, due to the rebound effect, technological progress has a slight negative impact on carbon emission efficiency. Energy consumption structure, government intervention, and foreign trade are all negative incentive factors. Therefore, efforts to improve carbon emission efficiency in the YEB should focus on transforming the economic growth model, adjusting the industrial structure, improving the energy consumption structure, and innovating green technology. The research results can provide a reference for the government policymakers to develop a green economy.

The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

According to the “dual-carbon” goal, the solution to achieving balanced regional development and industrial structural optimization while promoting sustainable development goals lies in the synergistic evolution mechanism of carbon emission efficiency and green technological innovation. Using provincial panel data from China from 2000 to 2022, this study calculates industrial structural optimization coefficients for the “advanced,” “rationalization,” and “ecology” dimensions. The impact of green technological innovation on industrial structural optimization is experimentally explored using panel regression, threshold effect, and mediating effect methodologies, based on the constraint perspective of carbon emission efficiency. The findings show that (1) the optimization of the regional industrial structure is successfully driven by both carbon emission efficiency and green technological innovation; (2) the impact of green technological innovation on industrial structural optimization is positively regulated by carbon emission efficiency, which also helps to achieve sustainable economic transformation; and (3) this moderating effect exhibits significant regional heterogeneity and U-shaped nonlinear characteristics, in the order of “central > west > east”. This study reveals how green technological innovation affects industrial structural optimization under the constraint of carbon emission efficiency. It offers reference recommendations for the creation of sustainable development policies in the future.

China is confronted with the dual constraints of economic transformation and carbon emission reduction. As the digital economy is a key force in promoting economic transformation and optimizing industrial structure, it is crucial to analyze the digital economy’s impact on carbon emission reduction from the perspective of energy consumption and industrial value chain implications. We selected data from 251 prefecture-level cities and above in China from 2011 to 2019 as research samples, measured the development level of the digital economy using the entropy value method, and constructed relevant regression models based on two-way fixed effects, intermediary analysis, and moderation analysis. The research reveals that: (1) The digital economy has a significant contribution to carbon emission efficiency, and there are significant regional heterogeneity and city size differences; (2) The digital economy can improve carbon emission efficiency by reducing energy consumption. (3) From a value chain perspective, industrial structure rationalization weakens the carbon emission efficiency improvement effect of the digital economy to a certain extent, whereas industrial structure upgrading obviously enhances the carbon efficiency improvement effect of the digital economy. The above findings enrich the research in the field of digital economy and environmental governance, contribute to a more comprehensive understanding of the mechanisms by which the digital economy affects the carbon emission efficiency, as well as provide policy implications for enhancing the use of the digital economy in the regional energy consumption and industrial value chain.