In the comprehensive advancement of rural revitalization, strengthening the development of regional public brands for agricultural and livestock products plays a critical role in increasing the income of farmers and herders. This study uses Inner Mongolia as a case and utilizes county-level panel data from 2012 to 2023. Employing empirical methods such as the fixed effects model, threshold effects model, and spatial econometric model, it examines the impact of regional public brand development on the income growth of farmers and herders. The results demonstrate that, in Inner Mongolia, the development of these regional public brands significantly boosts the income of farmers and herders by driving the growth of the agricultural industry. In terms of heterogeneity, the income-enhancing effect of regional public brands exhibits clear regional variation, and the impact on income growth markedly differs across different types of brands. Further analysis reveals that as the level of county urbanization increases, the income-enhancing effect of regional public brand development shows a continually strengthening trend. Moreover, this brand development exhibits a distinct spatial spillover effect; it not only promotes income growth within the brand's own region but also radiates to neighboring areas, driving up incomes in adjacent regions. Based on this analysis, this paper argues for persistently and steadily advancing the development of regional public brands for agricultural and livestock products. It is essential to actively promote the processing industry for these products, strengthen industrial chain integration, improve benefit-linking mechanisms and urban-rural coordination systems. By tailoring strategies to local conditions and sustaining long-term efforts, the brand effect can be substantially enhanced to better empower farmers and herders in increasing their incomes.

Forest therapy represents a key component of China’s ecological industry and plays a significant role in the rural revitalization strategy. Understanding the economic benefits accrued by local farmers through participation in forest therapy base development is essential for promoting sustainable rural development. Using survey data from 795 non-migrant farmers residing near forest therapy bases, this study examines the impact of forest therapy base development on household income, with distance to the bases serving as the instrumental variable. Control variables include individual characteristics, household attributes, farm types, and regional factors. The Endogenous Switching Regression Model (ESRM) is employed to estimate the causal impact of participation, while quantile regression is used to assess heterogeneity across participation types, farmer categories, and regions, followed by mechanism validation. The results reveal three key findings: (1) The Average Treatment Effect on the Treated (ATT) of employment participation is 0.3676, indicating a significant income boost for participating households. Compared to the counterfactual scenario, participation reduces income variability by 6.44%, suggesting higher and more stable household income, especially among new participants. (2) Heterogeneity analysis shows an inverted U-shaped impact: Forest therapy-based development participation most benefits middle-income farmers (QR_50). For agriculture-priority farmers, the impact is significant only among high-income groups (QR_75). Regionally, in western China, participation significantly raises income for low-income farmers (QR_25), while in eastern China, the largest gains are observed among middle (QR_50) and high-income (QR_75) farmers. Employment participation has a statistically significant effect on low-income (QR_25), middle-income (QR_50), and high-income (QR_75) households. In terms of the magnitude of the effect, government support has the strongest impact on low-income (QR_25) households. In the group with lower government support, employment participation has a significant effect on low-income (QR_25) and middle-income (QR_50) households, but the effect on high-income (QR_75) households is not significant. (3) Mechanism analysis indicates that both social network reinforcement (38.80% mediation) and ecological behavioral change (27.05% mediation) serve as significant partial mediators, the mechanisms of income enhancement operate through dual pathways. In conclusion, this study reveals that the income effects are characterized by two salient features: pronounced heterogeneity in benefit distribution among farmer groups, and divergent functioning of mediation pathways across regions. These findings underscore the need for targeted, context-specific policies to maximize the equitable and sustainable development of the forest therapy industry within the rural revitalization framework.

Under strict environmental regulations and intense market competition, resource-dependent enterprises face severe survival challenges. Achieving sustainable survival through the construction of internal capabilities, particularly in the absence of long-term external subsidies, represents an urgent conundrum for forestry enterprises. Integrating relevant economic and management theories, this study aims to elucidate the mechanism by which the "viability" of forestry-related leading enterprises influences their sustainable survival. Using a sample of 179 forestry-related leading enterprises in Inner Mongolia-a typical resource-rich region in China-and based on panel data from 2021 to 2023, we constructed a viability evaluation system encompassing factor endowment, technological innovation, and entrepreneurial traits, and conducted empirical analysis using statistical models. The results indicate that: (1) Enterprise viability is the core driving force promoting sustainable survival; (2) E-commerce adoption serves as a critical bridge connecting internal capabilities with external survival performance, playing a significant mediating role; (3) The higher the degree of external openness and the stronger the regional industrial comparative advantage, the more pronounced the promoting effect of viability. Furthermore, this promoting effect is significantly stronger in regions with lower ecological constraints and higher industrial agglomeration. This study suggests that policy formulation should shift from simple financial support to the cultivation of enterprises' endogenous capabilities. By supporting technological innovation, digital transformation, and optimizing the business environment, policies can facilitate the long-term sustainable survival of enterprises.

Technological innovation drives high-quality economic development, and artificial intelligence (AI) represents a new impetus for developing productive forces with new qualities. AI is becoming a focal point in economic development plans and national strategies worldwide due to its contribution to economic growth and the transformation of traditional production methods. This paper examines the impact and mechanism of AI on the export technological complexity of Chinese manufacturing enterprises from a corporate perspective. It utilizes data from listed manufacturing companies on the Shanghai and Shenzhen A-shares from 2008 to 2021 and employs a fixed-effects model. The results indicate that: (1) AI positively promotes the export technological complexity of Chinese manufacturing enterprises, with more pronounced effects in regions with higher export technological complexity. (2) Heterogeneity analysis indicates that AI significantly enhances the export technological complexity across various categories of enterprises. Particularly notable impacts are observed among state-owned enterprises, light textile enterprises, and enterprises located in the eastern and central regions. (3) Mechanism analysis reveals that AI indirectly promotes the export technological complexity of manufacturing enterprises by improving labor structure and enhancing corporate innovation capabilities. This study proposes relevant policy recommendations from four aspects: strengthening AI technology research and application, optimizing labor structure, enhancing corporate innovation development, and promoting balanced AI development.

Context: Sustainable intensification in the U.S. Midwest requires cropping strategies that enhance productivity and environmental outcomes without expanding land use. Winter oilseeds, particularly canola, are promising double crops in corn-soybean (CS) systems, yet their system-level (Corn-Canola-Soybean rotation system) impacts on yield, greenhouse gas (GHG) emissions, and profitability remain underexplored in the U.S. Midwest. Objective: This study assessed the agroecosystem implications of winter canola integration into CS rotations, focusing on trade-offs among productivity, net ecosystem carbon balance (NECB), GHG intensity, and profitability under alternative nitrogen (N) management strategies. Method: Using the process-based DayCent model, we simulated three two-year cycles (2019-2024) comparing conventional CS with corn-canola-soybean (CCS) systems across four N scenarios (0, 84, 140, and 167 kg N ha(-1)). Outputs were analyzed for crop yields, system-level biomass, NECB, GHG emissions, and net returns. Results and Conclusions: CCS systems exhibited yield penalties for corn (3-5%) and soybean (11-19%) due to shortened growing seasons, yet system biomass increased by 13-17% with canola inclusion. Higher N demand elevated total GHG emissions in CCS, but system-level GHG intensity remained stable because biomass gains offset emissions proportionally. NECB improved by up to 26.5% under moderate to high N inputs, reflecting stronger carbon retention. Net returns increased by 10-23% relative to CS, driven by canola as an added cash crop. Together, these findings demonstrate that winter canola integration can intensify land use while maintaining emission efficiency and enhancing both carbon outcomes and farm profitability. Significance: This study provides the first system-level assessment of winter canola in Midwestern CS rotations using process-based modeling. By linking agronomic management, environmental services, and profitability, it provides actionable insights for sustainable intensification and climate-smart diversification strategies that support U.S. decarbonization goals and Midwestern cropping system transitions toward resilient agricultural systems.

ABSTRACT The capacity to produce switchgrass efficiently and cost‐effectively across diverse environments can be pivotal in achieving the short‐ and medium‐term Sustainable Aviation Fuel targets set by the U.S. Department of Energy. This study evaluated the economic performance of forage‐ and bioenergy‐type switchgrass cultivars and their response to N fertilization under diverse marginal environments across the US Midwest that included Illinois (IL), Iowa (IA), Nebraska (NE), and South Dakota (SD). Data Envelopment Analysis (DEA) was used to evaluate the efficiency of 23 Decision‐Making Units (DMUs)—cultivar types and N fertilization rate combinations—while a cost–benefit analysis calculated their profitability over 5 years. Results showed that two energy‐type cultivars—“Independence” and “Liberty”—were superior economically to the forage cultivars. Independence performed best with the highest profit margin when fertilized at 56 kg N ha−1, particularly in the US hardiness zone 6a (Urbana, IL). Liberty exhibited the highest profit margins in hardiness zone 5b (Madrid, IA, and Ithaca, NE) at 56 kg N ha−1 and showed exceptional profitability with 28 kg N ha−1 in hardiness zone 6b (Brighton, IL). Switchgrass cultivar “Carthage” showed better efficiency score and profitability results in hardiness zone 4b (South Shore, SD) at 56 kg N ha−1. The profit trends observed in current study sites may indicate broader patterns across similar US hardiness zones. This study provides valuable insights for decision‐makers to optimize input strategies for biomass production of bioenergy switchgrass to meet renewable energy demands.

Switchgrass is a promising bioenergy feedstock due to its high biomass yield potential, adaptability to marginal lands, and low carbon intensity for feedstock production. However, accurate cost estimation and assessment of greenhouse gas (GHG) emissions for the energy-intensive harvesting process are essential for evaluating the sustainability of bioenergy. This study provides a comparative analysis of two harvesting methods: the Stepwise Method, which separates operations into multiple stages, and the Integrated Method, which combines mowing and raking into a single pass. The analysis was conducted under four scenarios based on field sizes and biomass yields. Using three years of field-scale switchgrass harvest data from 125 sites, GHG emissions, energy consumption, and harvesting costs were quantified using the GREET model and techno-economic analysis. Additionally, regression analysis identified key climate and operational factors affecting fuel consumption. The Stepwise method was the most cost-effective for large fields with high biomass yield, achieving the lowest harvesting costs ($37.70 per ton). In contrast, the Integrated Method performed better in small fields and low-yield conditions, reducing GHG emissions by 9 % and energy use by 5 %. Regression analysis confirmed that a larger field size reduced fuel consumption, while higher biomass yield and longer operational time increased fuel use. Maximum temperature also contributed to a slight increase in fuel consumption. These results provide actionable insights for optimizing harvesting strategies based on field-specific conditions and operational goals, contributing to the economic and environmental sustainability of bioenergy production.

Grassland ecosystems play a pivotal role in mitigating climate change via CO 2 sink. However, establishing robust methodologies for grassland carbon sink valuation remains complex scientific challenge. This study proposes an innovative application of fair value accounting principles to grassland carbon sink measurement, providing a multidimensional framework for ecological asset valuation. We develop a comprehensive theoretical foundation for fair value measurement through three analytical perspectives: cost-based, market-oriented, and income-driven valuation paradigms. Consequently, three valuation approaches are formulated: the Marginal Opportunity Cost (MOC) Method, Shadow Pricing (SP) Method, and Option Pricing (OP) Method. Through scenario-based comparative analysis, we demonstrate that methodological selection should be guided by carbon sink projects’ development stages and valuation objectives. Specifically, MOC excels in preliminary carbon pricing during project initiation phases, SP is optimal for mature primary markets with established transaction histories, while OP exhibits superior technical capabilities for projects’ future value estimation in secondary market transactions. This study provides practical insights for realizing ecological product value, advancing carbon market operations, and contributing to global climate change mitigation efforts.

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha−1). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R2 = 0.88). Cellulose was best predicted using the NDRE (R2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

Farmers’ participation in sustainable forest management plays a significant role in increasing their income and contributing to the comprehensive advancing of the rural revitalization strategy. This study focuses on farmers living near existing national forest health bases in Inner Mongolia. Using the endogenous switching regression model (ESRM), we empirically examine the income effects of farmers’ participation in sustainable forest management through employment and land leasing. The robustness of the model estimation is tested through various methods, including replacing the dependent variable. Furthermore, heterogeneity analysis is conducted using quantile regression. The results show the following: (1) Participation in sustainable forest management through employment (p < 0.001) and land leasing (p < 0.001) significantly increases annual household income by 4.28% and 1.44%, respectively. The income effect for farmers participating through employment is 2.84% higher than for those participating through land leasing. (2) For farmers who did not participate in sustainable forest management, the counterfactual scenario indicates a reduction in annual household income by 5.87% and 2.55%, respectively, highlighting a greater potential income improvement for non-participating farmers if they were to engage in sustainable forest management. (3) Heterogeneity analysis reveals that the income effects of the two participation forms vary across income levels. Employment participation in forest health bases has a more significant impact on low-income (QR_10) farmers, while land leasing participation has a greater impact on high-income (QR_90) farmers.

Purpose‐grown perennial herbaceous species are nonfood crops specifically cultivated for bioenergy production and have the potential to secure bioenergy feedstock resources while enhancing ecosystem services. This study assessed soil greenhouse gas emissions (CO 2 and N 2 O), nitrate (NO 3 ‐N) leaching reduction potential, evapotranspiration (ET), and water‐use efficiency (WUE) of bioenergy switchgrass ( Panicum virgatum L.) in comparison to corn ( Zea mays L.). The study was conducted on field‐scale plots in Urbana, IL, during the 2020–2022 growing seasons. Switchgrass was established in 2020 and urea‐fertilized at 56 kg N ha −1 year −1 . Corn management followed best management practices for the US Midwest, including no‐till and 202 kg N ha −1 year −1 fertilization, applied as urea–ammonium nitrate (32%). Our results showed lower direct N 2 O emissions in switchgrass compared to corn. Although soil CO 2 emissions did not differ significantly during the establishment year, emissions in subsequent years were over 50% higher in switchgrass than in corn, likely due to increased belowground biomass, which was over five times higher in switchgrass. Nitrate‐N leaching decreased as the switchgrass stand matured, reaching 80% lower than in corn by the third year. Differences in ET and WUE between corn and switchgrass were not significant; however, results indicate a trend toward reduced WUE in switchgrass under drought, driven by lower aboveground biomass production. Our study demonstrates that switchgrass can be implemented at a commercial scale without negatively impacting the hydrological cycle, while potentially reducing N losses through nitrate‐N leaching and soil N 2 O emissions, and enhancing belowground C storage.

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents’ income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers’ entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior.

This paper aims to theoretically and empirically test whether the Weather Index Insurance for Mutton Sheep (WIMS) which protects the herders from increased feeding costs resulting from drought and snow disasters promotes the income of herders in China’s Inner Mongolia or not. We have applied the OLS, PSM, and quantile regression models using field survey data from 261 herders in Xilin Gol, Inner Mongolia Autonomous Region for our objective. The findings demonstrate that the WIMS significantly increases the overall and high-income herders’ income, but has no significant effect on herders’ income at other income levels.. According to the results, we recommend that as the subsidized agricultural insurance product, the government should expand the pilot region of WIMS to furtherly test its income effect and consider reducing or exempting insurance premiums for low-income herders to reduce their financial pressure.

Domestic tourism plays a crucial role in the Australian economy, generating revenue, creating employment opportunities, fostering cultural identity, and facilitating tourism growth and development. The remote regions of Australia are particularly reliant on domestic inbound tourism to stimulate their local economies. This study investigates the influence of heritage sites and various factors on domestic tourism inflows to eight states in the Australia between 1998-2021. The gravity method and random effect model are employed for the empirical analysis. The results indicate that the macro determinants, including population of origin state, gross state product per capita, infrastructural development, shared border between states, and the number of heritage sites, have significant and positive impact on domestic tourism inflow. Conversely, the consumer price index, distance, and pandemic outbreak have a negative influence on domestic tourism inflow. These findings hold important practical implications. Given Australia's geographical remoteness, promoting domestic tourism becomes imperative to boost the tourism industry and local economies. Therefore, it is recommended that authorities prioritize domestic tourism flows and invest in infrastructure, preserve heritage sites, stabilize prices, implement effective marketing strategies, and respond swiftly to public emergencies such as the Covid-19 pandemic.

Promoting green production behavior among farmers is crucial for enhancing income, improving industrial efficiency, and ensuring ecological security. This study analyzes the determinants of green production behavior among farmers in Cheifeng, China, Using a binary logistic model on a sample of 860 microdata. The result of empirical analysis pointed out that the implementation rate of rural households was low at 54.5 %, highlighting the need for intervention. The results suggest that age, party members, total income, total area, irrigation conditions, market, policy, social factors, and knowledge transfer ability significantly influence green production behavior. Among these factors, age, party member, total area, irrigation conditions, and knowledge transfer ability have negative effects, while other factors positively affect green production. To promote green production behavior, this study recommends improving access to information, developing a training system, enhancing quality and safety information disclosure, establishing traceability mechanisms, and providing policy subsidies. This stud also highlights the need to strengthen publicity, technical guidance, and media persuasion for promoting the green production of Chinese medicinal materials.

In the last 30 years, grassland productivity has declined seriously due to climate variations and unreasonable human activities. Therefore, to analyze the impact of different factors on grassland productivity, we selected three grassland stations of the Typical Steppe from west to east and collected 38 years of data. The Pearson Correlation and Fixed Effect Model were used to analyze the impact of precipitation, temperature, and grazing intensity on grassland productivity. The empirical results show that precipitation positively and significantly affected grassland productivity. The effects of climate change are more significant than human activities, but the impact of temperature is greater than precipitation. The synergy between precipitation and temperature was greater than between precipitation and temperature separately. In addition, the effects of climate change and human activities on grassland productivity have evident regional heterogeneity. The variation trend gradually increases from west to east in factors that affect grassland productivity. Therefore, we suggest some implications for grassland risk management, such as utilizing some financial products for climate risk and focusing on the synergy index to design financial products, such as design weather derivatives. Lastly, we should strengthen the research on the relationship between climate change and grassland productivity to provide a scientific basis for revealing the intrinsic relationship between climate, human activities, and grassland productivity.

[目的]在建立健全草原风险管理体系,保障草原生态安全和稳定草原畜牧业生产的背景下,草原保险的作用更加凸显.草原风险评估和区划作为草原保险设计的前提和基础,其准确性直接决定着草原保险条款的合理性及科学性.[方法]文章利用半定量化因果关系矩阵法、HP滤波模型、多种聚类分析法和单因素方差分析法.[结果]可将锡林郭勒草原分为低、中、高和极高4个风险区,各风险区间的差异显著,其中,低风险区呈现出致灾因子危险性低、承灾体脆弱性低和防灾减灾能力强的显著特点;中风险区呈现出承灾体暴露性水平高,但致灾因子危险性处于中等水平的显著特点;高风险区呈现出致灾因子危险性高,防灾减灾能力处于中等水平的典型特征;极高风险区呈现出承灾体脆弱性水平高,防灾减灾能力弱的典型特征.[结论]在厘定草原保险费率时,应考虑多种风险间的交互作用,构建合理的草原风险区划图.

The agro-pasture ecotone is distributed all around the world. In these areas, the productive land forces are decreasing, and due to the irrational economic activities and the vulnerable ecological environment in these regions occurs land degradation. This study focuses on the effect of two different fattening approaches of beef cattle and output from the economic point of view by using a cost-benefit analysis technique in the eastern agro-pasture ecotone of Inner Mongolia, China. This study considers the environmental, social, and economic costs as input factors and concludes that both fattening systems have different characteristics. The result shows that the intensive farming system has more fluctuation and instability in terms of the number of animals due to the feed shortage in the local area. In comparison, the continuous fattening system is much more efficient and sustainable in terms of cost management and benefit analysis due to the local condition of the area. The empirical results indicate that the beef cattle industry has a high marginal return. Our research highlights the need to prioritize local resources and incorporate feed-intensity analysis in livestock.