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.

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.

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.

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.

The contradiction between coordinating the preservation and inheritance of traditional villages has recently garnered considerable attention in academic circles and government departments. The present study is based on data obtained from 1,000 villagers in 176 traditional villages who were surveyed in 2020. Using the Logit multiple regression method, the study quantitatively analyzed five closely related core explanatory variables of villagers in traditional villages and six factors that significantly influence the survival and development of these villages. The analysis was conducted before (2011) and after (2021) the preservation of traditional villages. Research indicates that (1) the economic income of farmers has rapidly increased and their sources of income have shifted from primarily farming to tourism development and breeding. The proportion of respondents expressing "satisfaction” and “very satisfaction" with both traditional village preservation and development was only 26.6%. (2) Among the core explanatory variables influencing farmers' willingness for preservation and inheritance, in terms of the impact mode, natural capital and human capital exhibited a significant negative effect, whereas material capital, financial subcapital, and social capital exhibited a significant positive effect. Material capital and financial capital positively influenced farmers' satisfaction at 1% significance level, whereas natural capital negatively affected their satisfaction at 5% significance level, and social capital positively influenced their satisfaction at 5% significance level. Regarding impact intensity, financial capital made the largest positive contribution (β = 18.323), followed by material capital (β = 15.597). By contrast, natural capital made the largest negative contribution (β = −7.012). Additionally, social capital, which reflects the development potential of villagers, exhibited a significant contribution value (β = 8.374, P > 0.05). Human capital had a negative impact, whereas social capital had a positive impact, with the effects of both being nonsignificant. (3) Among the six main control variables influencing traditional village preservation and inheritance, those with significant influences were economic factors and cultural and artistic factors (P < 0.05), whereas those with nonsignificant influences were political factors, social factors, ecological environmental factors, and geographical transportation factors (P > 0.05). Regarding the impact mode, all factors exhibited a positive and significant effect on farmers' willingness for preservation and inheritance. Regarding impact intensity, cultural and artistic factors exhibited the largest contribution (β = 26.112), followed by economic factors (β = 25.173) and political factors (β = 8.374). Moreover, ecological environmental factors, reflecting the livable environment for villagers, also made significant contributions (β = 5.213). Social factors and geographical transportation factors exerted positive effects, but their impact was not prominent. Accordingly, the paper proposes two insights: First, a reflection on the implementation of traditional village preservation policies—correctly managing the relationship between proactive and passive protection. The pros and cons of China's traditional village preservation policies must be re-examined, and a practical and effective mechanism for coordinating the interests of traditional village preservation must be established. Second, the preservation model for traditional villages in China needs to be re-evaluated. Exploring a model where the government leads, with farmers at the center, and various sectors of society actively participate is essential. Clarifying the relationship between the preservation and development of traditional villages is crucial, recognizing that preservation is the objective and economic development is the fundamental guarantee. (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.