Search Results (300)

Technological innovation in energy enterprises constitutes a pivotal component in realizing the transition to a green economy. In recent years, the complexity and volatility of the international economic landscape have significantly amplified economic policy uncertainty (EPU) across nations, which is poised to exert a profound influence on the technological innovation activities of energy enterprises. This study employs the Tobit regression method to investigate the relationship between EPU and corporate technological innovation (CTI), based on data from Chinese listed energy companies spanning the period of 2007 to 2018. Empirical results indicate that EPU exerts a significant positive influence on technological innovation for energy enterprises. Furthermore, we employed a Fisher permutation test to further elucidate the heterogeneity of this impact across various sub-industries, enterprise ownership types, and governance mechanisms. Specifically, EPU has a more pronounced promoting effect on technological innovation for traditional energy enterprises, non-state-owned enterprises, and enterprises with high failure tolerance. Against the backdrop of increasing global EPU, the findings of this study offer certain implications for governmental industrial policies and corporate governance mechanisms. Full article

The global shift towards sustainable energy and stringent climate policies has underscored the need for decarbonizing energy systems, electrifying transportation, and transforming supply chains. In this context, China’s new energy vehicle (NEV) industry, as the largest global producer and consumer of automobiles, is pivotal in advancing energy substitution and achieving carbon reduction goals. This study investigates the energy efficiency and supply chain transformation within China’s NEV sector, leveraging panel data from 12 representative provinces over the period 2017–2023. Employing a robust analytical framework that integrates the DEA-BCC model, Malmquist index, and Tobit regression, the study provides a dynamic and regionally differentiated assessment of NEV industry efficiency. The results reveal significant improvements in total factor energy efficiency, predominantly driven by technological progress. R&D intensity, infrastructure development, and environmental regulation are identified as key enablers of efficiency, while excessive government intervention tends to hinder performance. The findings offer valuable empirical insights and policy recommendations for optimizing China’s NEV industry in the context of energy system transformation and sustainable industrial development. Full article

Evaluating rural households’ subjective well-being (SWB) and identifying its determinants is crucial for rural sustainable development. This study takes Diqing Prefecture in the Tibetan region of China as a case, aiming to address two key research questions: (1) How do livelihood assets affect subjective well-being (SWB)—directly or indirectly—through the mediating role of the agricultural-income proportion? (2) Do these effects vary across different livelihood strategies? A questionnaire survey was administered to 489 randomly selected rural households in mid-2022. Two index systems were constructed: one for livelihood assets based on the Sustainable Livelihood Framework and another for SWB based on the Millennium Ecosystem Assessment. A subgroup Tobit regression model was utilized to analyze the heterogeneous effects. The results revealed deficiencies in SWB regarding basic material for a good life and health. Human, financial, and social assets are positively associated with SWB. However, natural assets directly negatively impact SWB across dimensions of basic material, security, and freedom, although the negative effect is masked by the mediating effect of farming livelihood strategies. Notably, human assets’ positive influence significantly strengthens with the agricultural income proportion rising. Whether physical, financial, and social assets positively affect SWB depends on farm work participation. These evidence-based findings contribute to a better understanding of the heterogeneous role of sustainable livelihoods in affecting rural households’ subjective well-being and highlight the need for policymakers to design diverse, targeted policies to support rural development. Full article

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide. Full article

Although corporate tax avoidance strategies may increase internal funding that supports innovation, they can also undermine it by weakening governance and encouraging short-term financial objectives. However, the overall impact remains theoretically contested, with insufficient empirical research available. This study examines the relationship between corporate tax avoidance and innovation, focusing on the moderating role of environmental, social, and governance (ESG) practices. Using a panel dataset of 12,408 firm-year observations of South Korean listed companies from 2014 to 2023, Tobit regression analyses reveal a statistically significant negative association between tax avoidance and innovation. Notably, this negative relationship is significantly mitigated in ESG-engaged firms, particularly those with stronger ESG performance. Further analysis indicates that these moderating effects are driven primarily by the social and governance domains. These findings suggest that ESG practices can offset the detrimental effects of tax avoidance by strengthening governance and stakeholder alignment. This study underscores the importance of integrating ESG principles into corporate tax strategies to support long-term innovation and sustainable corporate development. Full article

An in-depth and comprehensive evaluation of carbon emission efficiency (CEE) is essential for promoting high-quality development and achieving the “dual-carbon” goals. This study applies a super-efficiency slacks-based measure (Super-SBM) model with carbon emissions treated as an undesirable output to measure provincial CEE and the Malmquist–Luenberger (ML) index across 30 provinces and major comprehensive economic zones in China from 2010 to 2023. Efficiency trends for 2024–2025 are projected using a hybrid Autoregressive Integrated Moving Average (ARIMA)–Long Short-Term Memory (LSTM) approach. Furthermore, CEE patterns are examined at both national and regional levels, and the relationships between CEE and potential drivers are analyzed using Tobit regressions. Combining the regression outcomes with short-term forecasts, this study provides a forward-looking perspective on the evolution of CEE and its associated factors. The results indicate that (1) China’s CEE demonstrates a generally fluctuating upward trajectory, with the southern coastal and eastern coastal regions maintaining the highest efficiency levels, while other regions remain relatively lower. (2) The temporal changes in CEE across economic zones correspond to variations in technical efficiency and technological progress, with the latter contributing more prominently to overall improvement. (3) CEE shows significant associations with multiple factors: population density, economic development, technological advancement, government intervention, and environmental regulation are positively associated with efficiency, whereas urbanization tends to correlate negatively. Based on these findings, policy implications are discussed to promote differentiated pathways for enhancing CEE across China’s regions. Full article

Predicting delays in commercial flights is an increasing challenge due to rising air traffic demand, which generates additional costs and operational complexity. This study synthesizes and evaluates machine learning approaches for flight delay predictions, aiming to identify the most accurate prediction logic and assess the role of sample size in model performance. A systematic literature review was conducted, followed by a meta-analysis of 1077 studies published between 2015 and 2025. The studies were classified by prediction logic (binary classification or regression) and evaluated in terms of model effectiveness using Data Envelopment Analysis and Tobit regression to determine the influence of explanatory variables. The results show that binary classification approaches achieved higher average accuracy than regression models did, with confidence intervals validating their relative effectiveness. Furthermore, findings indicate that the use of more complex models does not guarantee improved predictive performance, suggesting that researchers should prioritize robust variable selection rather than constantly adopting increasingly complex methods. This work provides a comprehensive overview of machine learning methods for flight delay predictions and highlights implications for optimizing airport operations and enhancing passenger experience through the adoption of more reliable predictive strategies. Full article

The increasing need to optimize efficiency in port container terminals has led to the transition of operations from manual to automated or semi-automated processes. Automation involves integrating or gradually adopting digital technologies and equipment that reduce human intervention, enhance productivity, safety and sustainability. This study investigates the impact of automation on port efficiency through a comparative analysis of 20 container ports in the wider Mediterranean region, using a two-stage modeling approach. In the first stage, Data Envelopment Analysis (DEA) is applied under constant and variable returns to scale to estimate port efficiency using infrastructure, equipment, and container throughput data. The second stage employs Tobit regression to assess the effect of automated operations or systems on port efficiency, including variables such as the automation index, TEUs per employee, TEUs per ship (call) and revenue. A key contribution of this study is the development of a methodological framework for qualitatively classifying and evaluating these ports based on their level of automation, the introduction of digital technologies or equipment, and investments in new technologies. The results indicate that automation alone does not necessarily lead to higher efficiency unless it is effectively integrated into operations accompanied by adequate staff training and supported by gradual investment strategies. By contrast, cargo intensity (TEUs per call), highlights the importance of vessel size and cargo concentration in improving port performance. Full article

In order to alleviate the constraints of global warming and sustainable development, digitalization has made significant contributions to promoting agricultural carbon reduction through resources, technology, and platforms. Under this situation, China insists on developing agricultural scale management. However, what impact will scale management in agricultural digital emission reduction have on mechanisms and pathways? Based on three rounds of follow-up surveys conducted by the Digital Countryside Research Institute of Nanjing Agricultural University in Jiangsu Province from 2022 to 2024, in this study a total of 258 valid questionnaires on the rice and wheat industry were collected. Methods such as member checking and audit trail were employed to ensure data reliability and validity. Using econometric approaches including Tobit, mediation, and moderation models, this study quantified the Scale Management Level (SML), examined the mechanism pathways of digital emission reduction in a scaled environment, further demonstrated the impact of scale management on digital emission reduction, and verified the mediating and moderating effects of internal and external scale management. We found that: (1) In scale and carbon reduction, the SBM-DEA model calculates that the scale of agricultural land in Jiangsu showed an “inverted S” trend with SML and an “inverted W” trend with the overall agricultural green production efficiency (AGPE), and the highest agricultural green production efficiency is 0.814 in the moderate scale range of 20–36.667 hm². (2) In digitalization and carbon reduction, the Tobit regression model results indicate that Network Platform Empowerment (NPE) significantly promotes carbon reduction (p < 1%), but its squared terms exhibit an inverted U-shaped relationship with agricultural green production efficiency (p < 1%), and SML is significant at the 5% level. From a local regression perspective, the strength of SML’s impact on the three core variables is: NPE > DRE > DTE. (3) Adding scale in agricultural digital emission reduction, the intermediary mechanism results showed that the significant intensity (p < 5%) of the mediating role of Agricultural Mechanization Level (AML) is NPE > DTE > DRE, and that of the Employment of Labor (EOL) is DRE > NPE > DTE. (4) Adding scale in agricultural digital emission reduction, the regulatory effect results showed that the Organized Management Level (OML) and Social Service System (SSS) significantly positively regulate the inhibitory effect of DRE and DTE on AGPE. Finally, we suggest controlling the scale of land management reasonably and developing moderate agricultural scale management according to local conditions, enhancing the digital literacy and agricultural machinery training of scale entities while encouraging the improvement of organizational level and social service innovation, and reasonably reducing labor and mechanization inputs in order to standardize the digital emission reduction effect of agriculture under the background of scale. Full article

In the context of China’s “double carbon” goals, examining the spatial–temporal characteristics and influencing factors of the synergistic effect of pollution control and carbon reduction (SEPCR) in the Chengdu–Chongqing region (CCR) is crucial for advancing both air pollution (AP) control and carbon emissions (CE) mitigation. This study uses data on AP and CE from 2007 to 2022 and employs the coupling coordination degree (CCD) model, spatial autocorrelation analysis, and kernel density estimation to investigate the spatial–temporal distribution and dynamic evolution of the CCD between AP and CE in the CCR. Additionally, the Tobit regression model is applied to identify the key factors influencing this synergy. The results indicate that (1) during the study period, the air pollutant equivalents (APE) in the CCR showed a declining trend, while CE continued to increase; (2) the overall level of coupling coordination remained low, exhibiting an evolutionary pattern of initial increase, subsequent decrease, and then recovery, with synergistic effects showing slight improvement but significant fluctuations; (3) the SEPCR in the CCR was generally dispersed, exhibiting no significant spatial autocorrelation. A “core–periphery” structure emerged, with Chongqing and Chengdu as the core and peripheral cities forming low-value zones. Low–low clusters indicative of a “synergy poverty trap” also appeared; (4) economic development (PGDP), openness level (OP), and environmental regulation intensity (ER) are significant positive drivers, while urbanization rate (UR), industrial structure upgrading (IS), and energy consumption intensity (EI) exert significant negative impacts. Full article

To assist resource-based regions in overcoming the bottlenecks of industrial transformation and advancing high-quality development, this paper conducts an in-depth analysis of the internal mechanisms through which new quality productive forces contribute to high-quality development. Based on the construction of a measurement index system, a comprehensive measurement model is established, which includes three components: a coupling coordination degree model integrating the entropy method and grey relational analysis, an impact factor analysis model based on random effects Tobit regression, and a trend prediction model using the GM(1,1) approach. Taking Hebei Province as an example, an empirical analysis was conducted and relevant policy suggestions were proposed. The research findings are summarized as follows: (1) New quality productive forces promote high-quality development through driving, guiding, and synergistic mechanisms; (2) From 2013 to 2022, the coupling coordination degree across various cities in Hebei Province evolved from moderate imbalance to primary coordination, with the spatial pattern transitioning from “higher in the south and lower in the north” to a “central rise” phase, and finally to a stage of “all-round coordination”; (3) Forecast results indicate that inter-city coordination will continue to improve over the next five years; (4) Urbanization, scientific and technological innovation, and government intervention are identified as the core driving factors for promoting coordinated development. This study provides both theoretical methodological support and regional empirical evidence for the role of new quality productive forces in enabling high-quality development in resource-based regions. Full article

Understanding Farmer households’ subjective flood risk cognition is important for effectively mitigating the impacts of flood, and adequate disaster preparedness reduces the impact of floods on the sustainability of farmers’ livelihoods. The existing literature focuses on objective flood risk assessment and subjective–objective risk consistency and less systematically explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. Therefore, this study aims to explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. This study employed a random sampling method to conduct a survey among 540 households in Gaoxian County, Jiajiang County, and Yuechi County, which are flood-prone areas in Southwest China. Based on the survey results, this research framework can be used to evaluate systems of subjective flood risk cognition and farmers’ disaster preparedness. We chose the Tobit Regression Model to empirically explore the correlation between subjective flood risk cognition and farmers’ disaster preparedness. The results showed that among the 540 surveyed farmers, their overall subjective flood risk cognition was at a medium-high level (3.58), with self-efficacy more than response efficacy, more than threat, and more than probability. Further, the overall disaster preparedness of farmers was at a medium level (0.5), with physical disaster preparedness more than emergency disaster preparedness and more than knowledge and skills preparedness. The regression analysis showed that the probability of flooding and the threat in Farmer households’ subjective flood risk cognition were positively related to disaster preparedness, whereas self-efficacy, response efficacy, and overall risk cognition in Farmer households’ subjective flood risk cognition were negatively related to disaster preparedness. This study is representative of or may serve as a reference for building governance systems and disaster prevention in other flood risk areas in Southwest China. Full article

Enhancing tourism eco-efficiency (TEE) is crucial for achieving China’s “dual carbon” objectives. This study examines nine provinces in the Yellow River Basin from 2010 to 2022, employing a super-efficiency SBM model, kernel density estimation, gravity center migration, standard deviation ellipse, Tobit regression, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate spatial-temporal variations and influencing factors. The results show that TEE increased steadily before 2019, declined during the COVID-19 pandemic, and recovered after 2021. Spatially, widening disparities and a polarization trend were observed, with the efficiency center remaining relatively stable in Shaanxi Province. Factors such as advancements in tourism economic development, regional economic growth, technological innovation, and infrastructure improvements significantly promote TEE, whereas stringent environmental regulations and greater openness exert constraints, and the impact of human capital remains uncertain. Four types of condition combinations were identified—economic-driven, market-innovation-driven, scale-innovation-driven, and balanced development. Managerial implications highlight the need for region-specific pathways and regional cooperation, with a dual focus on technological and institutional drivers as well as ecological value orientation, to sustainably enhance TEE in the Yellow River Basin. Full article

Agriculture is fundamental to food security and environmental sustainability. Advancing its holistic ecological transformation can stimulate socioeconomic progress while fostering human–nature harmony. Utilizing provincial data from mainland China (2013–2022), this research establishes a multidimensional evaluation framework across four pillars: agricultural ecology, low-carbon practices, modernization, and productivity enhancement. Through comprehensive assessment, we quantify China’s low-carbon green agriculture (LGA) development trajectory and conduct comparative regional analysis across eastern, central, and western zones. As for methods, this study employs multiple econometric approaches: LGA was quantified using the TOPSIS entropy weight method at the first step. Moreover, multidimensional spatial–temporal patterns were characterized through ArcGIS spatial analysis, Dagum Gini coefficient decomposition, Kernel density estimation, and Markov chain techniques, revealing regional disparities, evolutionary trajectories, and state transition dynamics. Last but not least, Tobit regression modeling identified driving mechanisms, informing improvement strategies derived from empirical evidence. The key findings reveal the following: 1. From 2013 to 2022, LGA in China fluctuated significantly. However, the current growth rate is basically maintained between 0% and 10%. Meanwhile, LGA in the vast majority of provinces exceeds 0.3705, indicating that LGA in China is currently in a stable growth period. 2. After 2016, the growth momentum in the central and western regions continued. The growth rate peaked in 2020, with some provinces having a growth rate exceeding 20%. Then the growth rate slowed down, and the intra-regional differences in all regions remained stable at around 0.11. 3. Inter-regional differences are the main factor causing the differences in national LGA, with contribution rates ranging from 67.14% to 74.86%. 4. LGA has the characteristic of polarization. Some regions have developed rapidly, while others have lagged behind. At the end of our ten-year study period, LGA in Yunnan, Guizhou and Shanxi was still below 0.2430, remaining in the low-level range. 5. In the long term, the possibility of improvement in LGA in various regions of China is relatively high, but there is a possibility of maintaining the status quo or “deteriorating”. Even provinces with a high level of LGA may be downgraded, with possibilities ranging from 1.69% to 4.55%. 6. The analysis of driving factors indicates that the level of economic development has a significant positive impact on the level of urban development, while the influences of urbanization, agricultural scale operation, technological input, and industrialization level on the level of urban development show significant regional heterogeneity. In summary, during the period from 2013 to 2022, although China’s LGA showed polarization and experienced ups and downs, it generally entered a period of stable growth. Among them, the inter-regional differences were the main cause of the unbalanced development across the country, but there was also a risk of stagnation and decline. Economic development was the general driving force, while other driving factors showed significant regional heterogeneity. Finally, suggestions such as differentiated development strategies, regional cooperation and resource sharing, and coordinated policy allocation were put forward for the development of LGA. This research is conducive to providing references for future LGA, offering policy inspirations for LGA in other countries and regions, and also providing new empirical results for the academic community. Full article

The “three rights separation” system plays a vital role in enhancing the economic efficiency of rural residential land use, thereby contributing to land revitalization and rural-urban integration. Using survey data from 456 farmers in Yujiang District and Deqing County, this study employs DEA, Tobit, and threshold regression models to analyze the system’s effects. The results show that the system improves economic efficiency by approximately 8.9%, primarily by incentivizing investment and promoting land transfers. A nonlinear threshold effect exists: investment incentives become significant only when idle land exceeds 35 m², consistent with farmers’ economic decision-making. Land transfers enhance efficiency via marginal return equalization, however, economies of scale are not evident, being constrained by legal and coordination factors. The findings highlight the importance of deepening reform implementation, enhancing farmers’ understanding of property rights, adopting differentiated incentives tailored to land size and farmer capacity, and regulating the land transfer market to ensure transparency and fairness. Furthermore, promoting collective or service-based management models can help overcome natural scale limitations, thereby unlocking the system’s full institutional dividends. Full article