Super-SBM Model Research Articles

A Non-Linear Exploration of the Digital Economy’s Impact on Agricultural Carbon Emission Efficiency in China

As the global climate crisis intensifies, improving agricultural carbon emission efficiency has become crucial for achieving the sustainable development goals (SDGs). This study investigates the complex, non-linear relationship between China’s digital economy and agricultural carbon emission efficiency, utilizing panel data from Chinese provinces spanning 2012–2022. We employ a multi-method approach, including the Super-SBM model for efficiency measurement, two-way fixed effects models, quantile regression, and Generalized Additive Models (GAMs) for empirical analysis. Our findings reveal: (1) The digital economy significantly enhances agricultural carbon emission efficiency, but with distinct non-linear characteristics across different dimensions. (2) The impact varies among digital economy aspects: the digital economy foundation shows the most substantial influence, followed by the rural digital industry level, while rural digital infrastructure has a relatively minor effect. (3) A threshold effect is observed, with the digital economy’s impact more pronounced in regions with higher agricultural carbon emission efficiency. (4) GAM analysis unveils complex non-linear patterns: the rural digital industry’s impact initially decreases before increasing, the digital economy foundation shows an overall increasing trend with plateaus, and rural digital infrastructure exhibits a near-linear relationship. (5) Sensitivity analysis indicates that agricultural carbon emission efficiency is most responsive to changes in the digital economy foundation, followed by the rural digital industry level. These findings provide nuanced insights into the digital economy’s role in enhancing agricultural sustainability. We propose targeted policy recommendations, including accelerating rural digital infrastructure development, optimizing the rural digital industry structure, and implementing context-specific digital facility construction. These strategies aim to fully leverage the digital economy’s potential in improving agricultural carbon emission efficiency, contributing to China’s “dual carbon” goals and sustainable agricultural development.

Measuring the Ecoefficiency of Tourism in Typical Tourist Cities and Analyzing the Influencing Factors—Anhui Huangshan City as an Example

To protect the environment, promote sustainable tourism, and enhance the quality and efficiency of the tourism industry, accounting for and identifying the ecological efficiency of tourism is crucial. In this study, we constructed a scientific measurement system for assessing the ecological efficiency of tourism in Huangshan City from 2011 to 2022 using a Super-SBM (slack-based measure) model and a Tobit regression model. Our findings revealed the following: (1) The overall trend in the total efficiency in Huangshan City fluctuated considerably during the period under study but demonstrated an overall positive trend of development. Scale efficiency mostly showed consistent annual improvement, while pure technical efficiency remained relatively stable. (2) Economic, social, environmental, and human-resource- and transportation-related factors all exerted significant positive influences on the ecological efficiency of tourism in Huangshan City. Based on these results, in aiming to enhance the ecological efficiency of tourism in Huangshan City, comprehensively considering multiple factors, such as the economy, society, the environment, human resources, and transportation, is recommended. Attention should be paid to balancing industrial expansion with technological innovation while simultaneously investing in the development of education and human resources. Emphasis should also be placed on protecting and improving the environment alongside efforts to improve capabilities in technological innovation and the level of management. The research findings presented in this article offer a valuable theoretical underpinning, as well as practical guidance for Huangshan City and other representative tourist destinations on how they can enhance the efficiency of their tourism ecosystems, thereby facilitating high-quality and sustainable development within the tourism industry.

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions?

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

Measurement of green innovation efficiency in Chinese listed energy-intensive enterprises based on the three stage Super-SBM model

Measurement of green innovation efficiency in Chinese listed energy-intensive enterprises based on the three stage Super-SBM model

The effect and mechanism of digital economy on green total factor productivity — Empirical evidence from China

In the context of global digitalization, it is of great significance to promote the development of digital economy to reduce carbon emissions and improve green total factor productivity (GTFP). Based on the data of 30 provinces in China from 2011 to 2021, this study constructs a variety of methods, such as Super-SBM model, fixed effect model and intermediary effect model, to empirically test the impact and mechanism of digital economy (DIE) on GTFP. Based on the research findings, the growth of DIE contributes to the improvement of GTFP. Moreover, the impact of DIE on GTFP in eastern China is greater than that in central and western China. There are significant differences between the development level of DIE and GTFP in eastern, central and western regions. Further research shows that digital economy affects the improvement of GTFP through three intermediary variables: economic scale, industrial structure and technological innovation. This study provides empirical evidence to support the effective enhancement of GTFP in developing countries as the digital economy evolves. It provides effective recommendations for developing countries and emerging economies to develop a green economy. However, this study has limitations in data sample, research scope and mechanism analysis depth. Therefore, the conclusions drawn in this study can only provide empirical evidence for identifying the relationship between the DIE and GTFP to a certain extent. Future research should be expanded in these aspects.

Carbon Emissions and Its Efficiency of Tourist Hotels in China from the Supply Chain Based on the Input–Output Method and Super-SBM Model

Carbon Emissions and Its Efficiency of Tourist Hotels in China from the Supply Chain Based on the Input–Output Method and Super-SBM Model

GLOBAL–LOCAL DRIVING FORCES of CHINA’S GREENING of INDUSTRY

ABSTRACT The greening of industry is an inevitable path for developing economies to achieve sustainability transition. However, the specific roles of global and local driving forces are still not substantially understood. This study develops a three-dimensional framework, which includes coercive pressures, normative pressures, and technology acquisition to analyze the influence of global and local drivers on China’s greening of industry. We measure the industrial eco-efficiency (IEE) using Super-SBM model and analyze its influencing factors using the Tobit regression model at the provincial level in China from 2010 to 2020. China’s IEE evolution and its drivers are stage-specific and regionally differentiated, and differences also exist in the roles of global and local drivers. Developing countries initially start the green development of industry under global normative pressures through integration in the global production network. Thereafter, global coercive pressures and local forces, including domestic technologies, market demands, and effective regulations, drive the further greening of industry. The different potentials of regional coercive and normative pressures depend on industrial structures and socio-technical regimes. Policy recommendations for the greening of industry in developing economies are proposed.

Does Low-Carbon Transition Promote Regional Sustainable Development? Evidence from the Huaihe River Ecological Economic Belt

The Chinese government has adopted a significant low-carbon transition strategy aimed at enhancing resource efficiency, advancing ecological conservation, and augmenting societal well-being. This research employs the super efficiency SBM model to evaluate the ecological well-being performance (EWP) within the Huaihe River Ecological Economic Belt. Subsequently, it employs the Sys-GMM model and conducts a threshold effect analysis to regressively examine the impact of the low-carbon transition on EWP. The key findings are as follows. The EWP in the Huaihe River Ecological Economic Belt is relatively low; cities with higher economic development levels tend to exhibit lower EWP scores. The impact of the low-carbon transition on EWP is contingent upon the level of regional economic development and exhibits a singular threshold effect predicated on the economic development level as the threshold variable. Specifically, when economic development is at a low level, an intensification of the low-carbon transition results in a decline in EWP within the Huaihe River Ecological Economic Belt. Conversely, when the economic development level surpasses the threshold, the low-carbon transition facilitates the enhancement of EWP. This study’s principal contribution lies in elucidating the intricate relationship between the low-carbon transition and sustainable development.

Exploring the complex relationship between industrial upgrading and energy eco-efficiency in river basin cities: A case study of the Yellow River Basin in China

The synergistic advancement of industrial upgrading and energy eco-efficiency is crucial for the attainment of the Sustainable Development Goals. This study explored the coupling coordination relationship and its spatial-temporal evolution characteristics between industrial structure advancement and energy eco-efficiency, industrial structure rationalization and energy eco-efficiency in Yellow River Basin cities from 2010 to 2020. The super-SBM model and coupling coordination degree model were used to calculate the energy eco-efficiency and coupling coordination degree. Moran's I and Tobit model were used to analyze the spatial autocorrelation and the factors of coupling coordination degree. The coupling coordination degree of industrial structure advancement and energy eco-efficiency decreased from 0.535 to 0.489, with a growth rate of −8.60 %, showing a fluctuating downward trend. The coupling coordination degree of industrial structure rationalization and energy eco-efficiency decreased from 0.296 to 0.246, with a growth rate of −16.88 %, showing an "M" -shaped downward trend. The coupling coordination degree had a significant spatial correlation, and the global Moran's I value was stable around 0.2. Additionally, public transport and degree of opening-up can significantly promote the coordination relationship between industrial upgrading and energy eco-efficiency. These consequences can supply evidence for industrial and energy structure adjustment in river basin cities.

Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China

This study investigated the influence of cultural industry agglomeration on the energy carbon emission performance (CEP). Based on panel data from 276 cities in China, we used the Super-SBM model to measure the CEP. We then used the Tobit regression model to calculate the influence coefficient of cultural industry agglomeration and eight control variables on the CEP and analyzed the complex effects of cultural industry agglomeration on the CEP. The results showed that there is the phenomenon of “diseconomies of agglomeration” in cultural industry agglomeration, which cannot improve the CEP. For each unit of cultural industry agglomeration increase, the CEP decreases by 0.055; however, this phenomenon is not linear. Further research showed that the effects of cultural industry agglomeration showed a trend from good to inferior in the order of east, central, and west and did not improve with time. Finally, we used the panel quantile regression model and found that as the CEP levels rise, the negative impact of cultural industry agglomeration improves. Our research results show that strengthening the technical level to promote the upgrading of the cultural industry is the best way to achieve sustainable development. Governments at all levels should pay attention to the emission reduction potential of cultural industry agglomeration under high CEP levels and strengthen the benign agglomeration of the cultural industry.

Evaluation and Influencing Factors of Regional Green Innovation Efficiency Based on the Lasso Method

Regional green innovation efficiency is affected by multiple factors. Based on the undesirable output of 11 provinces in the Yangtze River Economic Belt from 2012–2021, this paper uses the Super-SBM model containing undesirable outputs to measure and analyze the regional green innovation efficiency and tests the variables affecting regional green innovation efficiency through lasso regression. Stepwise regression and least squares estimation are used to prove the rationality of the lasso regression model. The results show that the regional green innovation efficiency of the Yangtze River Economic Belt during 2012–2021 among different regions has differences that manifest as the efficiency of the lower, middle, and upper reaches decreases successively, and the differences decrease gradually over time. All the variables affecting the regional green innovation efficiency pass the lasso regression variable screening test, and most of the influencing factors positively promote the regional green innovation efficiency and are more explanatory than the stepwise regression and the least squares proof model.

Stimulating innovation or enhancing productivity? The impact of environmental regulations on agricultural green growth

A comprehensive understanding of the impact of environmental regulations on agricultural green growth is crucial for formulating appropriate policy designs and achieving effective climate change mitigation. This study, based on the Porter Hypothesis framework, aims to propose our hypotheses in the influence of environmental regulations on agricultural green growth. This study first utilizes the Super-SBM model combined with the global Malmquist-Luenberger index to measure agricultural green growth in G20 countries. Subsequently, the study analyzes the impact of environmental regulations on agricultural technological innovation and green growth, highlighting variations among countries with different development levels. The results indicate: (1) Significant differences exist in agricultural green growth among G20 countries, generally exhibiting an increasing trend. (2) Environmental regulations have a significant impact on both agricultural green growth and agricultural technological innovation, but the effects of different types of environmental regulations are various. Furthermore, different conclusions are observed for countries with heterogeneous development levels. (3) There is a lag effect in the impact of environmental regulations on agricultural green growth and technological innovation, highlighting the importance of pre-establishing an environmental policy framework. (4) An inverted U-shape relationship exists between environmental regulations and agricultural green growth, while no such relationship is found in agricultural technological innovation.

The collaborative agglomeration of industries and the realization of the digital economy on the green high-quality development of cities

PurposeThis study explores the role of industrial co-agglomeration and digital economy in influencing the green high-quality development of the Yellow River National Cultural Park to provide countermeasures and suggestions for promoting the whole-area high-quality development.Design/methodology/approachThis study is based on panel data from 56 cities from 2010 to 2022. First, a Super-SBM model is built to evaluate green high-quality development. Secondly, location entropy is used to measure industrial co-agglomeration and the entropy weight method is used to measure the digital economy. Finally, the panel Tobit model is used to analyze the impact of industrial co-agglomeration and digital economy on the green high-quality development of Yellow River National Cultural Park.FindingsThis study found that (1) industrial co-agglomeration has a negative implication in green high-quality development, while the digital economy boosts green high-quality development; (2) industrial co-agglomeration is a less critical dependency on the level of development of the digital economy in influencing green high-quality development, while the facilitating effect of the digital economy is more dependent on industrial co-agglomeration and (3) the trend of slow growth in industrial co-agglomeration and digital economy development, with significant regional differences in green high-quality development.Research limitations/implicationsUndeniably, our study has several limitations. Firstly, as the study area only includes some cities in individual provinces, such as Qinghai, this paper only analyzes at the city level, which does not better reflect the differences between provinces; secondly, this study only adopts one method to determine the digital economy. In the future, other methods can be explored to measure digital economy; finally, in addition to the main role of digital economy and industrial co-agglomeration, other factors may also affect the green high-quality development of YRNCP. Future research should introduce other variables to improve the theoretical framework.Practical implicationsFirst, it provides countermeasures and suggestions for promoting the green high-quality development of YRNCP. Second, it helps to implement the new development concept, cultivate the new quality productivity of culture and the tourism industry and promote the green high-quality development of YRNCP. Third, it provides references to improve the management measures and related policies of the YRNCP more accurately and efficiently. Fourth, it helps to build a new development pattern and has important practical significance in promoting the high-quality development of the whole basin, protecting and inheriting the Yellow River Culture and helping the Chinese-style modernization and development, which are of great practical significance.Social implicationsThe research is carried out from the new perspective of industrial co-agglomeration and digital economy, which provides the theoretical basis and reference for solving the problem of green high-quality development of YRNCP. Second, it broadens the research idea of green high-quality development. Third, it quantitatively analyzes the impact of industrial co-agglomeration and digital economy on the high-quality development of YRNCP, deepening the research on the green high-quality development of YRNCP. Fourth, it helps to enrich and improve the theoretical research related to the national cultural park development and has positive significance in promoting the management and innovation of the cultural industry and the construction of related disciplines.Originality/valueThe paper’s findings illustrate the functional relationship of the digital economy and industrial co-agglomeration with green high-quality development and propose countermeasures to facilitate the high-quality development of the Yellow River National Cultural Park.

Research on the dynamic characteristics and improvement path of urban sustainable development level: Based on gravitational model and scenario simulation method

The rapid expansion of cities has brought new challenges to sustainable development (SD). Therefore, how to balance between SD and the economic growth has become the key to regional development. With the aim of enhancing the research system of SD level, founded on the deep research of SD level from a new perspective of “input-output” and the dynamic evolution law of SD level, this paper has explored the path for cities to improve their level of SD to achieve regional coordinated development. By virtue of the Super-efficiency Slack-Based Measure model based on unexpected output (unexpected super efficiency SBM model), it has measured the SD level of 21 cities in Sichuan from 2010 to 2019. Using methods such as equilibrium coefficient, obstacle analysis and gravitational model, this paper has studied the spatiotemporal evolution law of SD level and constructed six scenarios to simulate the future trend of SD level. The results have indicated that the level of SD in Sichuan is showing a gradual decline with significant constraints. The focus of SD in Sichuan is gradually shifting from Chengdu to a city cluster centered around Chengdu, Ziyang, and Meishan. In an ideal development scenario, the level of SD has significantly improved. This study has revealed the current status and potential of SD in Sichuan. In the future, with more concentration on the role of core urban agglomerations, Sichuan should adjust policies to address input–output barriers to promote SD throughout the province.

Research on the Measurement of Gold Green Total Factor Productivity in the Yangtze River Economic Belt under the Background of Green Finance

With the continuous growth of the economy, the contradiction between finance and the environment has become increasingly prominent, and green finance has attracted much attention due to its sustainable development function. This study integrates green financial efficiency into green total factor productivity based on the two-stage super efficiency unexpected SBM model, and constructs the financial green total factor productivity of the Yangtze River Economic Belt from 2013 to 2021. The convergence model is used to verify the convergence of productivity development. The research results indicate that, in terms of time dimension, the efficiency of green finance in the Yangtze River Economic Belt is relatively stable, the level of financial ecological performance first decreases and then increases, and the financial feedback ecological performance remains at a high level for a long time. The overall trend of financial green total factors is increasing, and financial green technology innovation is an important driving factor for the increase; In terms of convergence testing, no significant alpha convergence was found in the upper, middle, and lower reaches of the Yangtze River Economic Belt, and the regional differences did not significantly decrease. However, there was overall beta convergence, indicating a "catch-up effect" from low productivity provinces to high productivity provinces.

Spatial correlation network and influencing factors analysis of supply efficiency of basic public service (SEBPS) in rust belt regions of China: An empirical study from Northeast China

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

Analysis of the spatio-temporal variability and convergence of green development efficiency of city clusters in the middle reaches of the Yangtze River in China

The advantageous geographical location of the middle reaches of the Yangtze River (YRMR) plays an important role in promoting high-quality development in China and accelerating the development of the Yangtze River Economic Belt(YREB). In the new stage of development, green development efficiency (GDE) is an important metric for assessing the balance between economic development and ecological civilization. Therefore, taking three city clusters in China's YRMR as the study area, this research employs the Super-SBM model to assess their GDE in 2010–2020 in order to analyze the problem of GDE at the city scale. On this basis, spatio-temporal analyses, dynamic evolution, and convergence analyses have been carried out using the Thiel index, GML index model, and convergence model. The findings indicate that overall GDE has not yet reached an effective level and generally exhibits a two-stage fluctuating upward trend. Spatially, the pattern of distribution is "high in the southwest, low in the northeast". The differences within the city clusters in the YRMR in terms of green development are gradually narrowing, with differences within the same regions contributing the most to overall differences. The GML index shows a trend of fluctuating growth, indicating that the overall GDE growth is in good condition. GDE has grown significantly as a result of technological advancements, with the Wuhan metropolitan area (cluster A) experiencing the fastest growth in this regard, followed by the Chang-Zhu-Tan and the Poyang Lake rim city clusters (clusters B and C). The YRMR and its three major city clusters exhibit both σ- and β-convergence patterns. The convergence of GDE is simultaneously affected by several factors, including economic development (RGDP), urbanization level (UR), industrial structure (IND), technological development (TEC), and the level of openness (LO). Regional heterogeneity also exists. The above study provides decision-making references and empirical evidence for promoting green development in China's city clusters in the YRMR and advancing the improvement of economic-environmental incongruence in this area.

The spatio-temporal variations of cultivated land compensation efficiency and its influencing factors in mainland China

Cultivated land, for which compensation is a practical approach to safeguarding, is a strategic and scarce resource. Evaluating the cultivated land compensation efficiency and identifying its influencing factors are of great significance for monitoring the effect of such compensation and improving the associated policy. A new multi-dimensional framework for estimating the cultivated land compensation efficiency was proposed. This framework not only emphasized the externality of cultivated land but also incorporated its ecological value. Cultivated land compensation efficiency in mainland China from 2000 to 2021 was estimated using this framework and the global Super-SBM model. The panel Tobit model was also adopted to analyze the factors influencing cultivated land compensation efficiency. The results show that (1) Over the past 22 years, the cultivated land compensation efficiency in mainland China initially declined but subsequently exhibited an upward trend, maintaining an average value of 0.724. (2) On a spatial scale, the cultivated land compensation efficiency exhibited pronounced regional pattern, with higher levels observed in the northeastern and southwestern regions of mainland China and lower levels in the eastern region. The cultivated land compensation efficiency in different provinces has distinctive patterns as well. Generally, the cultivated land compensation efficiency is relatively stable in most provinces, though few illustrate an uptrend. (3) Both the ratio of the population with tertiary education and above to the total population and the ratio of primary sector production to GDP affect cultivated land compensation efficiency positively, with the former being more significant. Conversely, GDP per capita, agricultural water consumption, the area affected by disasters, and the ratio of local expenditure on science, technology and education to total fiscal expenditure affect cultivated land compensation efficiency negatively. Therefore, a set of recommendations has been put forward, encompassing the optimization of provincial inputs and outputs, the reinforcement of inter-provincial coordination and collaboration, and the expansion of the talent pool of funds.

Research on Efficiency Evaluation and Influencing Factors of Green Economy Development in Central China

Behind decades of rapid economic growth in China is the massive consumption of resources and environmental damage. Since the establishment of China's "dual carbon" goals, it has profoundly influenced the development of green economy in the country. The central region of China plays an important role in carrying out the industrial development process in the eastern region, and the current status of green economy development in the central region has a significant impact on the overall green development. This article evaluates the efficiency of green economy development in 80 prefecture level cities in six provinces in central China from 2012 to 2022 using a super efficiency SBM model that includes unexpected outputs. The results show that the efficiency of green economy development in the central region is gradually improving, and there is an imbalance in development among prefecture level cities. Through the study of its influencing factors, it is found that the level of economic development, industrial structure, education level, financial support, and opening up to the outside world have a significant impact on the development of green economy in the central region.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.