Jian Li

Foreign direct investment (FDI), as a key driver of global factor mobility, significantly influences urban sustainable competitiveness (USC). This study investigates whether FDI can enhance USC amid potential environmental externalities and the moderating role of environmental regulation. Using panel data from 281 Chinese cities (2012–2020), we employ fixed-effects regression models with quadratic terms to capture the nonlinear relationship between FDI and USC. Our empirical analysis finds an inverted U-shaped relationship between FDI and USC, where FDI boosts USC up to a threshold beyond which its impact turns negative, aligning with the pollution haven hypothesis. Notably, environmental regulation moderates this relationship, with lax regulation enhancing FDI’s positive effects more effectively than strict policies. Regionally, this threshold effect is pronounced in China’s eastern cities but is less significant in the northeastern, central, and western regions due to limited FDI inflows. These findings offer essential insights for policymakers: implementing balanced environmental regulations can optimize FDI’s contributions, encouraging high-quality, sustainable investment that strengthens USC.

The effects of land transfer on agricultural carbon emissions and their underlying mechanisms must be investigated if we are to achieve sustainable development and environmentally friendly high-quality agricultural development. This research experimentally investigated the spatial impacts of land transfer on agricultural carbon emissions and their underlying causes using multiple econometric models based on provincial panel data covering the years 2010 to 2022. The results allow us to draw the following conclusions: (1) Land transfer significantly inhibits agricultural carbon emissions. This conclusion remained valid after various robustness checks, including a reduction in sample size, change model type, and adjustment of geographical regions. (2) Agricultural socialized services play a positive moderating role in the process of land transfer to curb agricultural carbon emissions. (3) Land transfer has a substantial spillover effect on agricultural carbon emissions, resulting in significantly reduced emissions in the immediate area and nearby regions.

As one of the important driving forces of sustainable development in coastal areas, foreign direct investment (FDI) has provided new ideas for exploring optimal strategies. This analysis explores the linear impact of FDI on sustainable development in coastal areas with 53 cities in China from 2012 to 2020. Accordingly, a dynamic panel smoothed transition regression (PSTR) model is used to analyze the non-linear impact of FDI on sustainable development, with transition mechanisms of industrial structure and technological innovation level. The findings reveal that the non-linear effect of FDI on the sustainable development of coastal areas is obvious. When the coastal area’s industrial structure is more optimized, and the level of technological innovation is higher, the promotion effect of FDI on sustainable development is more obvious. Further, the threshold effect of industrial structure and technological innovation is different. The threshold conversion rate of industrial structure is faster, but the threshold effect of technological innovation is stronger. Regionally, the impact of FDI on the sustainable development of coastal adjacent areas is significant, but not on the sustainable development of inland areas due to the few FDI inflows. This analysis offers guidance for policymakers to further develop the tertiary industry, increase financial investment in innovation in coastal areas and encourage enterprises to improve their independent innovation capacity.

The digital economy, closely linked to agricultural progress, plays a key role in reducing agricultural carbon emissions. By utilizing panel data between 2011 and 2021 from 30 Chinese provincial-level regions, the present work empirically assesses the direct impact and nonlinear spatial spillover effects of the digital economy on agricultural carbon emissions, where methodologies including fixed effects, threshold models, and spatial econometrics are employed. The results were as follows: (1) The digital economy led to remarkable inhibition of agricultural carbon emissions, which was consistent across various robustness checks. (2) The carbon reduction efficiency of the digital economy presented obvious spatial variation, which had a greater negative effect in eastern regions and major grain-producing regions. (3) The digital economy had a nonlinear impact on agricultural carbon emissions, and technological innovation played a threshold effect, showing an “inverted U-shaped” characteristic of increasing first and then decreasing overall. (4) This emission abatement effect also showed a significant spatial spillover aspect, meaning that the digital economy development in one area prominently abates agricultural carbon emissions in adjacent provinces. Our findings might provide a theoretical and empirical foundation for comprehending and addressing agricultural carbon emissions abatement in China from the digital economy perspective.

Market segmentation behavior has led to environmental issues, notably, carbon emissions, during China’s economic transformation, posing a significant challenge to ongoing economic progress. This study builds upon the existing literature and empirical analyses on market segmentation and carbon emissions, establishing a theoretical framework to understand the relationship between market segmentation and carbon emission intensity. Using a panel dataset covering Chinese provinces from 2003 to 2020, we systematically examined the impact and transmission mechanisms of market segmentation on carbon emission intensity. This study revealed the following findings: (1) there is a significant positive correlation between market segmentation and carbon emission intensity, which remains true after a series of robustness tests; (2) the promoting effect of market segmentation on carbon emission intensity in the eastern region is significantly lower than that in the central and western regions and, as time goes by, the promoting effect of market segmentation on carbon emission intensity becomes smaller and smaller; (3) resource mismatch and industrial structure are two important transmission channels through which market segmentation affects carbon emission intensity; (4) market segmentation demonstrated a distinct positive spatial contagion impact on carbon emission intensity. This study provides valuable policy recommendations to align with China’s ‘dual carbon goals’ by supporting a unified market and fostering low-carbon development. These recommendations aim to steer China’s economy toward a greener and low-carbon transformation.

Urban sustainable competitiveness (USC) is one of the important indexes to measure the high-quality development of cities in China. Meanwhile, foreign direct investment (FDI) plays the prominent role in improving urban sustainable competitiveness. Therefore, this analysis aimed to test the impact of FDI on the USC and its mechanism using the intermediary effect model with a sample of 282 cities in China during the period 2012–2018. The influencing mechanism includes the scale effect, the technological effect, and the structural effects. The results show that: first, FDI is significantly and positively related to the USC of China, and the scale, technological and structure effects all play a mediating role, with the scale effects being the most significant. Moreover, population size shows negative effect on the USC. Second, the impact of FDI on the USC is regionally heterogeneous. FDI can significantly improve the USC in the eastern region, but has no significant effects on the northeastern, central and western regions. Third, FDI in the eastern region affects USC through structural effects, while the scale and technological effects do not play a mediating role but both effects can directly affect USC. FDI in the northeast region still has the structural effect, but this structural effect does not indirectly affect USC, while FDI in the western region has both scale and structural effects. In addition, the technological and structural effects in the central region have a direct impact on USC, while the scale effect in the western region has a direct impact on USC. Therefore, the findings suggest that utilizing FDI should take into account regional characteristics in China.

Government intervention and structural transformation play an important role in both the economy and carbon emissions. Based on provincial panel data from China from 2003 to 2020, this paper employs econometric models to investigate the impact of government intervention and structural transformation on carbon emissions. In particular, structural transformation is divided into two indicators: The rationalization of the industrial structure and the upgrading of the industrial structure. According to the research findings, government intervention has significantly promoted carbon emissions and structural transformation has had dual effects on carbon emissions; meanwhile, the rationalization of the industrial structure has significantly increased carbon emissions, while the upgrading of the industrial structure has slowed down carbon emissions, with these findings passing the corresponding robustness test. The relationship between government intervention, structural transformation, and carbon emissions varies significantly over time and across regions. Further investigations revealed that government intervention and structural transformation have a significant impact on carbon emissions in various panel quantiles. Finally, the paper makes policy recommendations in order to provide empirical support for promoting China’s high-quality economic development and achieving the “double carbon” goal.

Self evaluation and peer evaluation in data envelopment analysis (DEA) are effective means to comprehensively reflect the efficiencies of decision-making units (DMUs). However, when some of the inputs and outputs of DMUs cannot be accurately observed, the traditional evaluation methods will lose their applicability. This paper attempts to treat the imprecise inputs and outputs as uncertain variables based on uncertainty theory and hence to propose a new uncertain DEA model for cross-efficiency evaluation via the evaluation of both self efficiency and peer efficiency. Moreover, the equivalent form and the proof of the new model are also presented for accurate calculation. Finally, a numerical example is given to illustrate the evaluation results.

When decision making units (DMUs) have internal structures with imprecise inputs and outputs, uncertain network data envelopment analysis (UNDEA) is appropriate to deal with the efficiency evaluation of these DMUs. However, a deep insight into clarifying the power’s differences between the internal structures of DMUs is a deficiency in the current UNDEA model. To address this issue, in this paper, we propose a new UNDEA model by differentiating the power asymmetry of each sub-stage with assumption of a two-stage system and demonstrate an additive relationship between stage 1 and stage 2 of each DMU. Moreover, the equivalent form and its proof of the new model are also presented for accurate calculation. Finally, a numerical example reflecting three different additive relationships between two sub-stages of DMUs is given to illustrate the results of evaluation.