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The integration of the digital transformation of agriculture and China's 'dual-carbon' strategy for agriculture is new momentum for developing agriculture, reducing rural income inequality, and improving the subjective well-being of farm households. This paper uses data from the China Family Panel Studies (CFPS) in 2014, 2016, and 2018 to examine the impact of internet use on rural income inequality at the household level. The results show that internet use can significantly reduce rural income inequality by enhancing information availability and farm households' nonfarm employment level. The results of a heterogeneity analysis reveal that the internet is more conducive to improving the income inequality of rural burdened families and families in Western China. Further analysis shows that improving income inequality can enhance the subjective well-being of farmer households. Accelerating the construction of digital villages, building internet skills cultivation systems, and focusing on the utility of internet use among vulnerable rural groups can reduce rural income inequality. [ABSTRACT FROM AUTHOR]

Under the background of "dual carbon" strategy, the integration of renewable energy adds volatility to the grid. Relying solely on generation-side resources for regulation is inadequate, necessitating a flexible demand response from diverse demandside resources. This paper employs a physical connection and information exchange between the distribution network and microgrids to leverage the advantages of centralized-distributed optimization. This establishes a coordinated demand response model between the distribution network and microgrids, gradually establishing a new type of distribution network that integrates interconnected grids and microgrids. This also necessitates the analysis of the response characteristics of various load resources within microgrids and the categorization and modeling of loads based on their response speeds. Additionally, a method for evaluating the multi-time scale schedulable capacity of microgrids is proposed. Finally, a coordinated demand response model between the distribution network and microgrids based on the schedulable capacity assessment is established. This model is validated through case studies, demonstrating its effectiveness. The coordinated demand response between distribution networks and microgrids enables them to operate in a collaborative and economically safe manner. [ABSTRACT FROM AUTHOR]

Driving enterprises to implement carbon emission reduction actions and reduce carbon emissions is a crucial research topic in achieving the carbon peaking and carbon neutrality goals. As a significant external environment factor influencing corporate behavior, can institutional pressures effectively promote enterprises to reduce carbon emissions? This study aims to probe into the impact and mechanism of three institutional pressures, namely coercive, mimetic, and normative, on corporate carbon emissions, taking Chinese-listed companies as the research object. The results indicate that coercive pressure is positively associated with corporate carbon reduction, while normative pressure has no significant impact. Furthermore, mimetic pressure impedes corporate carbon emissions. The mechanism test shows that carbon reduction is a mediator in the influence exerted by three institutional pressures on corporate carbon emissions. In response to coercive pressure and mimetic pressure, enterprises tend to reduce their carbon emissions by cultivating concepts related to carbon emission reduction. Concepts related to carbon reduction play a more dominant role than carbon reduction actions when both are mediators, with the former even replacing the latter. This research provides a new perspective for improving corporate environmental performance and corporate sustainability. [ABSTRACT FROM AUTHOR]

SignificanceWith the escalating global climate change and ecological pollution issues, the "dual carbon" target of Carbon Peak and Carbon Neutrality has been incorporated into various sectors of China's social development. To ensure the green and sustainable development of agriculture, it is imperative to minimize energy consumption and reduce pollution emissions at every stage of agricultural mechanization, meet the diversified needs of agricultural machinery and equipment in the era of intelligent information, and develop low-carbon agricultural mechanization. The development of low-carbon agricultural mechanization is not only an important part of the transformation and upgrading of agricultural mechanization in China but also an objective requirement for the sustainable development of agriculture under the "dual carbon" target.Progress]The connotation and objectives of low-carbon agricultural mechanization are clarified and the development logic of low-carbon agricultural mechanization from three dimensions: theoretical, practical, and systematic are expounded. The "triple-win" of life, production, and ecology is proposed, it is an important criterion for judging the functional realization of low-carbon agricultural mechanization system from a theoretical perspective. The necessity and urgency of low-carbon agricultural mechanization development from a practical perspective is revealed. The "human-machine-environment" system of low-carbon agricultural mechanization development is analyzed and the principles and feasibility of coordinated development of low-carbon agricultural mechanization based on a systemic perspective is explained. Furthermore, the deep-rooted reasons affecting the development of low-carbon agricultural mechanization from six aspects are analyzed: factor conditions, demand conditions, related and supporting industries, production entities, government, and opportunities.Conclusion and Prospects]Four approaches are proposed for the realization of low-carbon agricultural mechanization development: (1) Encouraging enterprises to implement agricultural machinery ecological design and green manufacturing throughout the life cycle through key and core technology research, government policies, and financial support; (2) Guiding agricultural entities to implement clean production operations in agricultural mechanization, including but not limited to innovative models of intensive agricultural land, exploration and promotion of new models of clean production in agricultural mechanization, and the construction of a carbon emission measurement system for agricultural low-carbonization; (3) Strengthening the guidance and implementation of the concept of socialized services for low-carbon agricultural machinery by government departments, constructing and improving a "8S" system of agricultural machinery operation services mainly consisting of Sale, Spare part, Service, Survey, Show, School, Service, and Scrap, to achieve the long-term development of dematerialized agricultural machinery socialized services and green shared operation system; (4) Starting from concept guidance, policy promotion, and financial support, comprehensively advancing the process of low-carbon disposal and green remanufacturing of retired and waste agricultural machinery by government departments.

This study defines Provincial Carbon Emission Characteristics (PCEC) as the socioeconomic conditions influencing provincial carbon emissions, the potential for carbon neutrality, and carbon management strategies. Given China’s commitment to peak carbon emissions and to achieve carbon neutrality, the development of a robust and versatile PCEC evaluation system is imperative. However, previous studies that rely on single-objective evaluation models are limited. To address this gap, we propose a multi-objective PCEC evaluation system using a novel three-layer approach. This approach enhances the scientific rigor, comprehensiveness, and comparability of PCEC evaluation indicators (PCEC-EIs) by segregating the extraction process into three layers: (1) a factor layer, which identifies critical socioeconomic conditions; (2) a quantitative layer, which selects optimal quantitative indicators for each identified condition; and (3) a standardized layer, which standardizes these indicators, thereby improving their comparability. By applying hierarchical cluster analysis, our study classified 30 Chinese provinces into 7 distinct clusters, each with unique carbon emission characteristics. Further investigation of these clusters across various climate governance issues revealed the versatility of our multi-objective PCEC evaluation system. Our system supports the identification of provinces qualified to reach the emission peak earlier than 2030, differentiation of carbon intensity reduction efforts during the carbon peaking stage, and exploration of various emission reduction pathways to achieve carbon neutrality. Hence, our findings can guide the creation of bespoke emission-reduction strategies for each province; thus, aiding China’s national ambition to achieve peak carbon emissions and carbon neutrality. Importantly, our three-layered approach has broader implications beyond China, potentially assisting other regions or countries in grappling with similar climate-mitigation challenges.

• analyze the energy efficiency of Beijing's industrial sectors from 2002 to 2018. • model industry heterogeneity of Beijing's industrial sectors accurately. • low energy efficiency of Beijing's industrial sectors due to management inefficiency. • the energy efficiency of heavy industry is higher than that of light industry. • explore the influencing factors of energy efficiency of Beijing's industrial sectors. Improving energy efficiency is one of the effective means to achieve China's carbon peaking and carbon neutrality goals. As the capital of China, Beijing should improve energy efficiency and has a primary responsibility in achieving the goals. Considering the heterogeneity of industrial sectors, this paper proposes a data envelopment analysis (DEA) approach to analyze the total factor energy efficiency and its influencing factors of 31 industrial sectors in Beijing from 2002 to 2018. We show that the low energy efficiency of Beijing's industrial sectors, mainly due to management inefficiency, is not conducive to the realization of the goals. Besides, the energy efficiency of heavy industry is higher than that of light industry. The expansion of enterprise scale has a positive impact on energy efficiency, while the property right structure, foreign trade and energy consumption structure have a negative impact on energy efficiency. Finally, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

China's carbon peaking and carbon neutrality targets have created huge challenges for all the economic sectors in China, including the shipping industry. Various emission reduction measures, such as Waste Heat Recovery Systems (WHRSs), Drag Reduction Coatings (DRCs), and Slow Steaming (SS), are the main options for container ship companies to select in advance. This paper aims to find the optimal combination of measures for container ship companies to reach the carbon reduction targets, which are mainly set according to the carbon reduction requirements of the International Maritime Organization (IMO). A 0–1 integer programming model is proposed under the Maritime Emissions Trading Scheme (METS) to help container ship companies select the optimal combination of measures in the context of carbon peaking and carbon neutrality. Our results show that combination 6 (using a WHRS alone and a 5.0% reduction in the original speed) is the most suitable solution with the selected values of parameters. Sensitivity analyses of the parameters are performed, such as bunker price, the auction and purchase prices of carbon and incentive levels. From the sensitivity analysis, it is found that using a WHRS is the optimal combination of abatement measures within the fluctuation range of the parameters. At the same time, according to the results, container ship companies could choose the most appropriate and profitable strategy in the dual-carbon context. Therefore, container ship companies and policymakers have access to relevant carbon reduction suggestions to encourage the implementation of carbon reduction initiatives. [ABSTRACT FROM AUTHOR]

The adoption of new energy vehicles (NEVs) can effectively reduce vehicle exhaust emissions and achieve carbon peaking and carbon neutrality goals in the transportation sector. To facilitate the development of NEVs, the Chinese government issued the dual credit policy (DCP). However, whether the DCP can promote the technological innovation of NEVs and effectively reduce carbon emissions in the transportation sector remains to be studied. This study constructed the decision-making model of NEVs under the DCP and obtained the optimal strategy to study the impact of the DCP on carbon emissions. Furthermore, we constructed a bargaining game model based on an alliance strategy to demonstrate the coordination of the NEV supply chain. The results showed that implementing the DCP can effectively reduce carbon emissions in the transportation field. The higher the technological innovation credit coefficient or credit price, the more significant the DCP’s incentive effect on reducing carbon emissions. Decentralized decision-making weakens the DCP’s incentive effect on reducing carbon emissions. The bargaining game based on alliance negotiation can enable independent companies to achieve carbon emission reduction when making centralized decisions so that the DCP’s incentive effect on reducing carbon emissions is optimized. The alliance between manufacturers is not to increase profits but to enhance their product advantages. However, suppliers can gain higher profits by participating in the alliance, which provides a theoretical reference for the alliance’s cooperation in decision-making.

Consolidating carbon sink capacity and reducing carbon pressure are important channels to achieve the carbon peaking and carbon neutrality goals actively yet prudently. In order to study the current situation of carbon pressure in the Northwestern Sichuan, we took the carbon pressure of the Aba Tibetan-Qiang autonomous prefecture (Aba prefecture) as an example and used the Intergovernmental Panel on Climate Change (IPCC) approach to measure the carbon emissions, carbon uptake, and the carbon balance index (CBI) of each county-level city in Aba prefecture from 2012 to 2020. The study found that: (a) There was a continuous trend of declining carbon emissions, increased carbon uptake, and decreased CBI in Aba prefecture during the sample period, but there is a large variability among county-level cities; (b) Aba prefecture differs in the spatiotemporal distribution of carbon emissions, carbon uptake, and CBI. Based on the research results, we propose several optimized paths for alleviating the current carbon pressure situation in the Northwestern Sichuan. [ABSTRACT FROM AUTHOR]

Although shipping is a relatively energy-saving and environmentally friendly mode of transportation, the growth rate of its energy consumption and carbon emissions far exceeds that of other industries. As an important response to climate change, shipping decarbonization is not only an important part of achieving the temperature control goal of the Paris Agreement but is also an important direction for the future development of China’s ecological civilization construction. China has formulated and promulgated legislation and policies on shipping decarbonization both at the national and local levels. The proposal in 2020 of the goal of carbon peaking and carbon neutrality has accelerated this process. In this context, this paper aims at reflecting on legislation and policies for decarbonization of shipping under China’s “double carbon” target, and proposing suggestions for improvement. Firstly, we systematically review China’s legislation and policies on shipping decarbonization to outline the normative system of China’s shipping carbon reduction. Secondly, this paper evaluates China’s legislation and policies on shipping decarbonization from the perspective of both achievements and challenges. Finally, this paper proposes that China’s legislation and policies for decarbonization of shipping should be further improved from two aspects: enhancing mandatory force and expanding normative content.

The Yellow River Basin (YRB) serves as a critical energy hub in China. Enhancing energy efficiency in cities across the YRB is pivotal for achieving high-quality development and meeting carbon peaking and neutrality targets. This paper proposes a multidimensional technology heterogeneity assessment framework to refine the classification of production technologies. It leverages this framework to develop a data envelopment analysis model for evaluating green total factor energy efficiency (GTFEE) in 78 cities within the YRB from 2006 to 2022. The key findings are as follows: (1) Grouping outcomes from the multidimensional heterogeneity framework significantly differ from existing literature. (2) GTFEE shows notable spatiotemporal variations within the YRB, with a substantial overall increase since 2011. However, cities in the central YRB exhibit lower GTFEE performance. (3) Energy inefficiency in groups 1, 2, and 3 is attributed to developmental inefficiency, management inefficiency, and practical inefficiency, respectively. (4) Evaluation of energy-saving potential reveals an average annual potential of around 8 million tce per city during 2006–2022. The study concludes with strategies for enhancing GTFEE based on dynamic GTFEE decomposition outcomes, supporting policy design for achieving dual-carbon goals through improved energy efficiency in the YRB region. • Technology heterogeneity grouping results differ from those in literature. • GTFEE within the Yellow River Basin exhibits significant spatiotemporal variations. • Sources of energy inefficiency vary among the groups. • Average annual energy-saving potential of approximately 8 million tce per city during 2006–2022. • GTFEE improvement path is devised according to the decomposition of MMGTFEEI. [ABSTRACT FROM AUTHOR]

Prioritizing the reduction of carbon emissions in key developing urban centers is a significant step toward achieving China's carbon peaking and neutrality goals. We examined the Chengdu metropolitan area as a case study. We proposed a coupled method for inversely estimating nighttime light and carbon emissions from energy consumption, enhancing the accuracy of fitting at the county scale and addressing the issue of missing energy consumption data. We integrated the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) model for comprehensive analysis, mitigating the limitations arising from the use of a singular model. Furthermore, we employed a multi-scenario analysis with an extended STIRPAT model to forecast the carbon emission trajectory of the Chengdu urban agglomeration and elucidate potential pathways for achieving carbon peaking and carbon neutrality goals. The findings indicated the following: (1) carbon emissions are predominantly concentrated in regions characterized by high levels of urban development. Forested land serves as the primary carbon sink; however, its capacity in the Chengdu metropolitan area fails to offset carbon emissions from energy consumption; (2) factor decomposition analysis revealed that economic development was the principal driver of carbon emissions, leading to an increase of 23.7964 million tons. Conversely, energy intensity emerged as the primary mitigation factor, reducin g carbon emissions by 11.0677 million tons; (3) The Chengdu metropolitan area is projected to reach a carbon peak by 2030, with the multi-factor coordinated optimization (MFO) model representing the most favorable trajectory, with a projected peak of approximately 15.6998 million tons. However, to achieve carbon neutrality by 2060, there is still a surplus of approximately 10.4808 million tons. • The carbon sink effect of forests is insufficient to offset the carbon emissions generated by energy consumption. • Multiscale analysis of carbon emission status. • Four scenario models have been developed to explore the path of regional scientific emission reduction. • Achieving carbon neutrality in the study area presents a challenge. [ABSTRACT FROM AUTHOR]

The Healthy China 2030 Initiative is closely related to the coordinated development between national health, economy, and society. This major move demonstrates China's active engagement in global health governance and in the fulfillment of the 2030 Agenda for Sustainable Development (SDGs). Based on Grossman's health production function, this paper introduces key factors such as environmental pollution and environmental regulation to empirically investigate the regulating effect of environmental regulation, as well as the spatial spillover of environmental pollution and environmental regulation acting on resident health. We examine these effects by using the panel data of 28 cities of the urban agglomeration in the middle reaches of the Yangtze River (UAMYRY) between 2009 and 2019. The results show that: (1) Environmental pollution brings a loss to resident health. Among the urban agglomerations, the circum-Changsha–Zhuzhou–Xiangtan urban agglomeration (CCZXUA) and the Poyang Lake urban agglomeration (PLUA) have a much lower health effect of environmental pollution than the Wuhan urban agglomeration (WUA). (2) With the growing intensity of environmental regulation, the negative effect of environmental pollution on resident health will gradually decrease. Regionally, the environmental regulation in the CCZXUA has the best effect on residents' health, followed by the WUA and the PLUA, which have the worst. (3) As a whole, the spatial spillover of environmental regulation and pollution has a significant impact on residents' health, and the spatial spillover effect between urban agglomerations is stronger than that between cities in each urban agglomeration. The conclusions remain robust with various tests such as replacing control variables, introducing lagged explanatory variables, and considering endogeneity. Based on robust empirical evidence, several specific region policy suggestions, including rolling out proper environmental regulation policies, and establishing a linking mechanism of environmental management, were put forward to improve the environmental pollution state and resident health level of the UAMYRY. [ABSTRACT FROM AUTHOR]

With the introduction of “dual carbon” goals of “peaking its carbon emissions before 2030 and achieving carbon neutrality by 2060" and the gradual promotion of the carbon trading market, it is the main trend to achieve low-carbon and clean development in the future. It is of great practical significance to effectively measure the low-carbon development level and conduct obstacle degree analysis. Based on this, this article aims to investigate the low-carbon development level index of Fujian province from 2006 to 2019 by using the entropy weight TOPSIS model. Then, the key influencing factors are explored by using the obstacle degree model. The results show that the low-carbon development level index of Fujian province shows a fluctuating upward trend, rising from 0.164 in 2006 to 0.803 in 2019. The environmental system has always been an important factor affecting the low-carbon development in Fujian province, with an average annual obstacle degree of 46.52%, but the impact of the other three subsystems is also gradually increasing. Green innovation, energy conservation, air pollution, economic development, and environmental governance are the top five indexes with the highest annual obstacles. Based on the previous results, this article puts forward corresponding policy recommendations, which provide theoretical support for the low-carbon development of Fujian province.

China’s carbon peaking and carbon neutrality targets have created huge challenges for all the economic sectors in China, including the shipping industry. Various emission reduction measures, such as Waste Heat Recovery Systems (WHRSs), Drag Reduction Coatings (DRCs), and Slow Steaming (SS), are the main options for container ship companies to select in advance. This paper aims to find the optimal combination of measures for container ship companies to reach the carbon reduction targets, which are mainly set according to the carbon reduction requirements of the International Maritime Organization (IMO). A 0–1 integer programming model is proposed under the Maritime Emissions Trading Scheme (METS) to help container ship companies select the optimal combination of measures in the context of carbon peaking and carbon neutrality. Our results show that combination 6 (using a WHRS alone and a 5.0% reduction in the original speed) is the most suitable solution with the selected values of parameters. Sensitivity analyses of the parameters are performed, such as bunker price, the auction and purchase prices of carbon and incentive levels. From the sensitivity analysis, it is found that using a WHRS is the optimal combination of abatement measures within the fluctuation range of the parameters. At the same time, according to the results, container ship companies could choose the most appropriate and profitable strategy in the dual-carbon context. Therefore, container ship companies and policymakers have access to relevant carbon reduction suggestions to encourage the implementation of carbon reduction initiatives.

With the establishment of carbon peaking and carbon neutrality goals in China, developing low-carbon cities has become crucial. Previous studies primarily focus on the quantitative structure of land use to achieve low carbon optimization, while ignoring the spatial integration between urban form and carbon emissions. To fulfill this gap, this paper proposed a dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model, utilizing idea-point multi-objective linear programming (IMLP) and non-dominated sorting genetic algorithm II (NSGA-II) to optimize the low-carbon-emission pattern of the city at both the structural and spatial scales respectively. By innovatively linking the urban shape index with carbon emissions, this model aims to minimize urban carbon emissions, while maximizing economic development, urban development suitability, and the compactness of city. The proposed method is applied to Wuhan's main urban area in China, the DOLA model shows significant improvements in target values across all scenarios. Among them, the scenario that prioritized low carbon emissions at both the structural and spatial scales achieved the most significant reduction of 6.43 % in carbon emissions. The DOLA effectively contributes to achieving multiple spatial targets, thereby aiding in reducing carbon emissions. It offers technical support for developing pathways towards China's "dual carbon" target. • A dual-scale low carbon oriented multi-objective land use allocation (DOLA) optimization model is proposed. • The DOLA model innovatively incorporates urban form indices to carbon emissions. • The DOLA model achieves spatial aggregation targets, thereby facilitating significant reductions in carbon emissions. • The DOLA model generates a series of land use optimization scenarios, offering a valuable reference for land use planning. [ABSTRACT FROM AUTHOR]