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Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries. • Text mining method was used to explore the port integration policy evolution. • Port integration policy evolved from specific and simple to abstract and complicated. • Port development enriched from basic infrastructure to the governance model. • The evolution of port integration policy was related to reducing carbon emissions in the port sector. • The evolution of port integration policy made the ports intelligent in association with digital technology such as big data and AI. [ABSTRACT FROM AUTHOR]

Forest carbon sink project (FCSP) has received growing attention for its outstanding advantages in carbon emission reduction, environment protection, and achieving the carbon neutrality objective. Using a government-social capital cooperation model, FCSP helps to revitalize the ecological and economic benefits of the forest with standard and efficient management. However, the management and operation barriers of FCSP implementation are complex, multiple and indefinite in the long run, which requires a comprehensive barrier evaluation framework. Hence, this paper is devoted to constructing a barrier evaluation framework for FCSP implementation in China with an integrated multi-criteria decision-making (MCDM) method. To this end, critical barriers are identified and the criteria system is constructed with a two-stage systematic literature review and interaction with the experts. Later, an applicable and comprehensive barrier evaluation framework is built, wherein the interval type-2 fuzzy set (IT2FS) is used to process fuzziness and uncertain information, the best-worst method (BWM) is adopted to determine the criteria weight, and the Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II) is introduced for comprehensive barrier evaluation. The criteria weighting results indicate that FCSP implementation encounters challenges taking into account institutional and governance barriers (criteria weight: 0.3592), management barriers (0.2384), economic and market barriers (0.1637), knowledge barriers (0.0747), technical and infrastructure barriers (0.1639), C1 "Lack of clear leadership and policies" and C5 "Forest management performance" were determined the most significant criteria, A5 and A4 respectively performed the lowest and the highest barrier level. In sensitivity analysis, the ranking results were stable when the criteria changed, and Spearman's rank correlations of B2, B4, and B5 are significant. The results were validated by a comparative analysis with other methods with the help of Spearman's rank correlation coefficient, which reflects accepted high correlations (Spearman's pro values of BWM-IT2F-PROMETHEE II (Gaussian rule), BWM-IT2F-TOPSIS, and BWM-TFN-VIKOR are 1.00, 1.00, 1.00). The findings of the paper help the decision makers to develop more rational strategies and successfully implement the FCSP. [ABSTRACT FROM AUTHOR]

• China's sustainable development relies on agricultural trade and carbon emissions. • Carbon emission transfer index in agricultural trade is measured. • Decoupling-recoupling model and integrated prediction model are constructed. • A unified framework for trade, environment and technology is established. Under the new development pattern of domestic and international "dual circulation" in China, studying the relationship among green technology innovation, agricultural trade deficit (ATD) and carbon emission transfer embodied in agricultural trade (ATCT) becomes crucial for achieving coordinated and unified high-quality economic development and efficient environmental protection. Based on this, the paper uses environmental input–output model, "two-stage" decoupling and recoupling model, panel vector auto-regressive model, spatial econometric model and "rolling" Grey-BP neural network prediction model to explore present status of ATD, ATCT and their relationship, driving role of green technology innovation, as well as whether that relationship will be improving in the future. The results reveal the following: (1) China's ATD and ATCT show an increasing trend year by year, and their inter-provincial differences are gradually significant over time. The overall relationship between ATD and ATCT does not display ideally, which makes it necessary to quickly transform that relationship into reasonable and ideal state. (2) Green technology innovation has significant positive "U-shaped" and inverted "U-shaped" local effects on ATD and ATCT respectively, with a reasonable range of ATD reduction while ATCT increase valuing from 0 to 0.290, and has significant positive "U-shaped" spatial spillover effect on both without any reasonable range. The short-term effect of green technology innovation on coordinated promotion between ATD and ATCT is superior to the long-term one. (3) Green technology innovation has a significant regional heterogeneity. Regional differences must be taken into consideration seriously, and the measures adapted to local conditions are crucial. The role of fundamental and progressive green technology innovation has heterogeneity, which means that targeted development and utilization of green technology products are important. (4) Regardless of the time horizon, the relationship between ATD and ATCT will improve, which can provide a key step for coordinated development of economy and environment in agricultural trade. Therefore, in order for China to implement and promote further opening-up, reduce agricultural trade dependence, ensure food security and important agricultural product supply, as well as achieve the unity of high-quality economic development and ecological efficient protection, it is necessary to reasonably reverse trade deficit and increase carbon emission transfer in agriculture. [ABSTRACT FROM AUTHOR]

As China's largest source of carbon emissions, the power sector's transition to lower levels of carbon emission intensity is critical to achieving China's carbon emission (CE) reduction commitments. In this paper, the CE intensity of electricity generation (CEIE) in 30 provinces from 2000 to 2019 is calculated, and the temporal evolution and spatial distribution characteristics of CEIE are discussed. Then, the spatial spillover effect of each driving factor on CEIE is calculated using the spatial Durbin model. The CEIE reveals a first increasing and then decreasing trend, and the regional differences gradually decrease. Simultaneously, the CEIE has a significant positive spatial correlation, and the overall spatial distribution pattern is high in the northeast and low in the southwest of the country. Economic development and foreign trade both inhibit CEIE, while electricity consumption, research and development, unit operation level, and power structure all promote CEIE. Each driving factor's influence on regional CEIE exerts a spatial spillover effect. Among them, the spatial positive spillover effects of industrial structure and environmental protection investment are the most obvious, while the spatial spillover effects of other driving factors are not apparent. Furthermore, these findings provide practical insights into policy adjustments and the coordinated interregional development of low-carbon electricity. [ABSTRACT FROM AUTHOR]

• The factors that affecting urban carbon emissions efficiency (CEE) are explored. • The carbon emissions of 282 cities are estimated with the aid of nighttime light data. • Urban CEE has significant and positive spatial spillover effects. • Industrial agglomeration and collaboration are key to green production efficiency. This paper explores the factors that improve the efficiency of urban carbon emissions. Based on nighttime light data, the carbon emissions of 282 cities in China from 2004 to 2018 are estimated, and the carbon emission efficiency (CEE) is measured. The spatial autocorrelation results show that CEE in Chinese cities has significant and positive spatial spillover effects. The LISA low-low type cluster area of CEE presents an evolutionary trend of westward migration, and eventually formed two low-efficiency centers in the Central Plains city group and the Sichuan-Chongqing city group. The empirical results of the spatial Durbin error model show that urban population expansion, economic growth and R&D investment have effectively improved CEE, but industrial scale and agglomeration adversely affect CEE. In addition, human capital investment and foreign investment have not exerted their due green growth effects. Manufacturing agglomeration had a negative impact on local CEE, but had a warning effect on neighboring regions. Different from the manufacturing industry, the agglomeration of producer services industry can bring significant improvement to CEE, accompanied by a demonstration effect which can promote the CEE in adjacent regions. Further, this paper finds that developing the synergy between manufacturing and producer services industries can significantly restrain the negative effects of industry on CEE. This paper enriches the performance evaluation framework of low-carbon city development in theory, and varies the situations that previous researches only concerned about carbon emissions while ignoring emissions efficiency. This paper is a beneficial policy practice of the coordinated development of economic growth and ecological environmental protection in the regional coordinated development mechanism, which indicates that strengthening industrial collaboration can effectively improve the efficiency of green production in cities. [ABSTRACT FROM AUTHOR]

With the gradual strengthening of environmental supervision, how to coordinate the relationship between environmental protection and full employment has become an urgent problem. This paper takes China's carbon emission trading policy as the research subject, and evaluates the employment effect of carbon emission trading policy from the perspective of micro enterprises under the premise of considering the spatial spillover effect. This paper aims to verify and evaluate the spatial spillover effect of China's carbon emission trading pilot policy on labor demand of enterprises, and study the effect of environmental policies on the employment market in China. Firstly, from the theoretical point of view, comparisons among the differences of the average employment effect, spillover effect and total effect coefficient of carbon trading right policy are conducted under the condition of spillover effect or not. Secondly, from the empirical perspective and relaxing the STUVA conditions, a Triple Difference Model is constructed to evaluate the impact of carbon emission trading policy on the labor demand of enterprises by using the panel data of China A-share listed companies in Shanghai and Shenzhen from 2008 to 2017. Thirdly, this paper further uses the synthetic control method to analyze the employment effect differences of carbon emission trading policies in different pilot areas of China. The results show that the carbon emission trading policy in China has an about 10 percent positive spillover effect on the neighboring provinces in pilot areas. Moreover, the results indicate that China's carbon emission trading policy has generated some employment dividend, and the total effect is about 11.5 percent when the ratio of individual sample is considered. In addition, the impact of carbon emission trading policy on employment is gradually increasing, and the employment effect of carbon trading policies in different pilot areas of China is quite dissimilar. The findings of this paper confirm the positive employment effect of China's carbon emission trading policy, and have important policy implications for the further promotion of the national carbon emission trading market. At the same time, it helps to clarify the relationship between environmental protection and full employment. • Verifies and evaluates the spatial spillover effect of carbon trading policy. • Constructs a Triple Difference Model for evaluating the impact of carbon trading policies on the labor demand of enterprises. • Finds about 10% positive spillover effect of the carbon emission rights trading policy in China. • Shows about 11.5% total employment effect of carbon emission trading policy when the individual sample ratio was considered. [ABSTRACT FROM AUTHOR]

Achieving the peak of carbon dioxide (CO 2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO 2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO 2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO 2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025; (2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO 2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO 2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO 2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance. • Providing empirical studies regarding the mechanism of the inhibitory effect of green finance on CO 2 emissions. • Using systems dynamics model to address the missing data problem. • Making solid suggestions under COVID-19 Epidemic. • Building the relationship between the green finance system and CO 2 emissions and making forecasts for "3060" mission result. [ABSTRACT FROM AUTHOR]

By using Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, this paper explores the influence of electricity generation and technological innovation on CO 2 emissions, as well as that of population and economic growth, in China during the period of 1985–2019. Econometric methods of smooth transition regression (STR) are used to calculate the threshold effects of the linkage among the selected variables. The conversion function of LSTR(1) (logistic STR with one regime switch) is selected by F-statistics based on third-order Taylor approximation. The results of the diagnostic test indicate that when the growth rate of electricity generation exceeds 8.914%, the relationships become nonlinear. Moreover, 1% increases of electricity generation and technological innovation may lead to increases of 2.91664% and 0.31016% in China's CO 2 emissions, respectively, while a 1% increase in economic growth can decrease China's CO 2 emissions by 1.16441%. Finally, some implications are suggested, such as strengthening the publicity surrounding environmental protection to reduce the footprint of CO 2 emissions, extending the green GDP even further, and building a more diversified power supply structure. [Display omitted] • Nonlinear STR model is established based on STIRPAT model in China. • Positive impacts of population, electricity, and innovation on emissions are found. • Economic growth may lead to emissions reduction. • Translation of technological innovation should be advocated. [ABSTRACT FROM AUTHOR]

China has a large population and limited land resources. The dearth of available land poses considerable challenges for economic development and environmental protection. In order to realize maximum benefits through the optimization of land use structure, land use structure efficiency (LUSE) becomes an important planning guideline for the sustainable development of a region. This paper conducts a case study in the Middle Reaches of the Yangtze River (MRYR) and investigates the LUSE of the region considering the effect of carbon emissions from 1995 to 2018 by using the slack-based measure with undesirable output (SBM-Undesirable) model and Markov transition probability matrix with spatial effect. The results show that: (1) the average LUSE in the study area is 0.652 when the carbon emissions are not considered, compared with that of 0.569 when the carbon emissions are factored in. The comparative result indicates that carbon emissions have a great impact on the LUSE; (2) the LUSE has a rising trend during the study period (except for 2010) starting with a value of 0.463 in 1995 and ending with a value of 0.671 in 2018. Furthermore, the spatial pattern of the LUSE in the study area has been through a drastic change, with the high-value areas clustered at a single center in 1995 evolving to a multiple-center pattern in 2018; (3) High LUSE areas have a positive spillover effect on its neighboring areas, while low LUSE areas have negative spillover effects. The results can provide insights into the optimization of land use structure and the efficient utilization of land resources. This study also suggests that policy makers should optimize the industrial structure and propose policy initiatives oriented towards goals of energy-saving and emission reduction to facilitate regional planning and sustainable development. • LUSE was measured with the SBM-Undesirable model. • The effect of carbon emissions had a great impact on the LUSE. • LUSE had a rising trend in the MRYR from 1995 to 2018. • Markov transition probability matrix with spatial effect was used to reveal spatial association characteristics of LUSE. • LUSE had a significant spatial spillover effect. [ABSTRACT FROM AUTHOR]