Search Results (355)

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

Green innovation is a key driver of sustainable development, yet Chinese firms, as major innovators, still underperform in this area. While directors play a central role in corporate governance, the influence of their green backgrounds on green innovation remains underexplored. This study investigates how directors with green backgrounds impact corporate green innovation. We consider both the appointment and the power of green-background directors. At the same time, we use the manually collected data from China’s heavily polluting listed firms between 2014 and 2020. We also conduct regulatory effect and mediation effect analyses. We found the following: (1) Green-background directors significantly promote corporate green innovation. Appointing directors with environmental expertise enhances firms’ green innovation performance, and this positive effect strengthens as these directors’ power increases. (2) Mechanistically, green-background directors facilitate green innovation by raising firms’ environmental awareness and helping secure government environmental subsidies. (3) Contextual influences matter. Moderating effect tests reveal that the impact of green-background directors is strengthened in firms with diligent boards, firm size, and green investors, but weakened in regions with higher marketization levels. (4) Further analysis shows that green-background directors enhance both strategic and substantive green innovation while also ensuring the long-term continuity of green innovation efforts. Full article

This study intends to portray how varying degrees of environmental policy stringency and the growing pressure of global competition reflect on high-tech (HT) sectors’ cost rationalization strategies and lead to environmental consequences in 15 G20 countries (1992–2019). Moreover, we center the pattern of cost rationalization management regarding the opportunity cost of ecosystem service consumption and propose to test the fundamental hypothesis stating the possible transmission of competition-induced technological innovations to green economic transformation. Our new methodology estimates quantile-specific effects with MM-QR, while identifying the main interaction effects between regulatory pressure and trade competition uses an extended STIRPAT model. The results reveal a paradoxical finding: despite higher environmental policy stringency and opportunity costs of ecosystem services, HT sectors persistently adopt environmentally detrimental cost-reduction approaches. These findings carry important policy implications: (1) environmental regulations for HT sectors require complementary innovation subsidies, (2) trade agreements should incorporate clean technology transfer clauses, and (3) governments must monitor sectoral emission leakage risks. Our dual machine learning–econometric approach provides policymakers with targeted insights for different emission scenarios, highlighting the need for differentiated strategies across clean and polluting HT subsectors. Full article

China’s National Famous, Special, Excellent, and New Agricultural Products are key rural economic assets, yet their spatial patterns and sustainability drivers remain underexplored. Based on the geospatial data of 1932 National Famous, Special, Excellent and New Agricultural Products in China, this study systematically analyzes their spatial distribution pattern by using GIS spatial analysis techniques, including the standard deviation ellipse, kernel density estimation, geographic concentration index and Lorenz curve, and quantitatively explores the driving factors of sustainable development by using geographic detectors. The research results of this paper are as follows. (1) The spatial distribution shows a significant non-equilibrium characteristic of “high-density concentration in the central and eastern part of the country and low-density sparseness in the western part of the country” and the geographic concentration index (G = 22.95) and the standard deviation ellipse indicate that the center of gravity of the distribution is located in the North China Plain (115° E–35° N), and the main direction extends along the longitude of 110° E–120° E. (2) Driving factor analysis showed that railroad mileage (X10) (q = 0.5028, p = 0.0025 < 0.01), highway mileage (X11) (q = 0.4633, p = 0.0158 < 0.05), and population size (X3) (q = 0.4469, p = 0.0202 < 0.05) are the core drivers. (3) Three-dimensional kernel density mapping reveals that the eastern coast and central plains (kernel density > 0.08) form high-density clusters due to the advantages of the transportation network and market, while the western part shows a gradient decline due to the limitation of topography and transportation conditions. The study suggests that the sustainable development of National Famous, Special, Excellent, and New Agricultural Products should be promoted by strengthening transportation and digital logistics systems, enhancing cold-chain distribution for perishable goods, tailoring regional branding strategies, and improving synergy among local governments, thereby providing actionable guidance for policymakers and producers to increase market competitiveness and income stability. The study provides a quantitative, policy-oriented assessment of China’s branded agricultural resource allocation and its sustainability drivers, offering specific recommendations to guide infrastructure investment, e-commerce logistics enhancement, and targeted subsidy design for balanced regional development. The study highlights three key contributions: (1) an innovative integration of geospatial analytics and geographical detectors to reveal spatial patterns; (2) clear empirical evidence for policymakers to prioritize transport and digital logistics investments; and (3) practical guidance for producers and brand managers to enhance product market reach, optimize supply chains, and strengthen regional competitiveness in line with sustainable development goals. Full article

This study investigates the influence of agricultural mechanization on maize productivity in Tanzania’s Ruvuma region, a major maize-producing area vital to national food security. It addresses gaps in understanding the cumulative effects of mechanization across the maize production cycle and identifies region-specific barriers to adoption among smallholder farmers. Focusing on five key stages—land preparation, planting, plant protection, harvesting, and drying—this research evaluated mechanization uptake at each stage and its relationship with yield disparities. Statistical analyses using Python libraries included regression modeling, ANOVA, and hypothesis testing to quantify mechanization–yield relationships, controlling for farm size and socioeconomic factors, revealing a strong positive correlation between mechanization and maize yields (r = 0.86; p < 0.01). Mechanized land preparation, planting, and plant protection significantly boosted productivity (β = 0.75–0.35; p < 0.001). However, harvesting and drying mechanization showed negligible impacts (p > 0.05), likely due to limited adoption by smallholders combined with statistical constraints arising from the small sample size of large-scale farms (n = 20). Large-scale farms achieved 45% higher yields than smallholders (2.9 vs. 2.0 tons/acre; p < 0.001), reflecting systemic inequities in access. These inequities are underscored by the barriers faced by smallholders, who constitute 70% of farmers yet encounter challenges, including high equipment costs, limited credit access, and insufficient technical knowledge. This study advances innovation diffusion theory by demonstrating how inequitable resource access perpetuates low mechanization uptake in smallholder systems. It underscores the need for context-specific, equity-focused interventions. These include cooperative mechanization models for high-impact stages (land preparation and planting); farmer training programs; and policy measures such as targeted subsidies for harvesting equipment and expanded rural credit systems. Public–private partnerships could democratize mechanization access, bridging yield gaps and enhancing food security. These findings advocate for strategies prioritizing smallholder inclusion to sustainably improve Tanzania’s maize productivity. Full article

With the shift of the global innovation model from traditional closed-loop to open ecosystems, knowledge sharing and collaborative cooperation among firms have become key to obtaining sustainable competitive advantages. However, existing studies mostly focus on the static structure, and there is an insufficient exploration of the dynamic evolutionary mechanism and multi-party game strategies. In this paper, a two-dimensional analysis framework integrating the evolutionary game and the Lotka–Volterra model is constructed to explore the behavioral and strategic evolution of core enterprises, SMEs and the government in the innovation ecosystem. Through theoretical modeling and numerical simulation, the effects of different variables on system stability are revealed. It is found that a moderately balanced benefit allocation can stimulate two-way knowledge sharing, while an over- or under-allocation ratio will inhibit the synergy efficiency of the system; a moderate difference in the knowledge stock can promote knowledge complementarity, but an over-concentration will lead to the monopoly and closure of the system; and the government subsidy needs to accurately match the cost of the openness of the enterprises with the potential benefits to the society, so as to avoid the incentive from being unused. Accordingly, it is suggested to optimize the competition structure among enterprises through the dynamic benefit distribution mechanism, knowledge sharing platform construction and classification subsidy policy, promote the evolution of the innovation ecosystem to a balanced state of mutual benefit and symbiosis, and provide theoretical basis and practical inspiration for the governance of the open innovation ecosystem. Full article

This study analyses the impact of agricultural greenwashing on financial performance via green innovation. To this end, it employs data from Chinese A-share agribusinesses from 2012 to 2022. The study indicates the following results: (1) the practice of greenwashing (ESG disclosure–performance gap, GW) has a significant negative impact on ROA, particularly in non-state firms; (2) green innovation (patents, GI) partially mediates this relationship, with a percentage of 9.09%, as GW diverts research and development resources toward image management. Robustness checks are employed to confirm the results obtained using ROE and lagged models. Property rights moderate the effects: non-state firms are more adversely affected by innovation dependency, while state firms are protected by policies. The “double-edged” mechanism elucidates GW’s short-term legitimacy gains in contrast to long-term innovation suppression and financial decline. The report calls for the establishment of standardised ESG metrics (for example, the disclosure of pesticide residue) and targeted green incentives (for example, SME R&D subsidies) to be aligned with UN SDGs 9.4 (green tech) and 12.6 (responsible production). The present study offers insights into the governance of environmental, social, and governance (ESG) matters within the context of agriculture in China. Full article

As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes. Full article

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment. Full article

China has effectively safeguarded biodiversity by building the world’s largest system of nature reserves, but the livelihoods of farmers near the reserves are often not guaranteed. This paper aimed to deeply explore the intrinsic relationship between the geographical location of households and their livelihood outcomes within seven nature reserves across three provinces in China. Innovatively, this study subdivided households’ livelihood outcomes into four patterns: high well-being with high dependency (H-H), high well-being with low dependency (H-L), low well-being with high dependency (L-H), and low well-being with low dependency (L-L), in order to comprehensively analyze the diversity of households’ livelihoods and further reveal the spatial logic and mechanisms underlying regional development imbalances. Methodologically, a combination of quantitative analysis and qualitative research was adopted. Representative villages in the protected area and outside the protected area were selected for semi-structured interviews with the village heads. Meanwhile, farmers were randomly selected in the villages for structured interviews and 1106 questionnaires were collected. Through variance analysis, the study first identified the unique advantages of H-H-pattern households in natural resource utilization. Subsequently, a multinomial logistic model was used to deeply analyze how geographical location (including whether a household was located within a protected area and the distance to markets) affected the transition mechanisms of the other three livelihood outcomes towards the H-H pattern. Based on this, marginal effect analysis was employed to further delineate the specific influence pathways of geographical factor changes on households’ livelihood outcome selection probabilities. The results showed that the geographical location of households’ residences had a significant impact on their livelihood outcomes. For households in the L-L and H-L patterns, proximity to markets could significantly increase the probability of their livelihood transitioning to the H-H pattern. Meanwhile, residing within protected areas significantly promoted the transition of L-L and H-L households to the H-H pattern but showed a certain inhibitory effect on L-H households. Marginal effects analysis further shows that both living in protected areas and reducing distance to markets increase the tendency of households to be highly dependent on natural resources for livelihood outcomes. Compensation policies should be designed according to local conditions, and subsidies for the development of ecotourism and other service industries should be increased for rural households in protected areas to ensure sustainable development rather than transfer payments. Full article

In the fast-growing new energy vehicle (NEV) industry, selecting an appropriate technological innovation strategy is vital for enterprises to achieve a competitive market position while effectively coordinating their resources to align with their technical capabilities. This paper integrates ambidextrous innovation theory and the resource-based view to propose a configurational model that examines how the synergy between technological innovation and resources influences NEV firm performance. Using regression analysis and qualitative comparative analysis (QCA) for 52 listed Chinese NEV companies, this study uncovered multiple growth paths and mechanisms. The findings include the following: (1) No single factor was a necessary condition for performance, but effective combinations of innovation strategies and resource elements led to multiple success paths. (2) Government subsidies and R&D investment emerged as key drivers of performance. (3) Four distinct configuration paths were identified, with variations across firms with different resource bases. (4) In response to reduced government subsidies, NEV firms must shift from policy-driven strategies to resource- and market-driven innovation approaches. These insights provide strategic guidance for NEV enterprises in selecting innovation strategies suited to their unique resource bases in the evolving post-subsidy market environment. Full article

Climate policy uncertainty (CPU) poses formidable challenges to global sustainable development and corporate strategic planning, while intelligent transformation is emerging as a pivotal enabler of organizational sustainability. Using panel data from Chinese A-share listed companies between 2011 and 2022, this study investigates the impact of climate policy uncertainty on intelligent transformation. The results indicate that CPU significantly promotes corporate intelligent transformation, a conclusion that remains robust under various sensitivity tests. Government innovation subsidies, enterprise absorption capacity, and enterprise human capital positively moderate this facilitating effect. A heterogeneity analysis reveals that the effect of CPU on intelligent transformation is more pronounced among firms in sci–tech finance pilot zones, regions with high digital financial inclusion, and those led by CEOs with banking experience. This paper contributes to the literature on climate policy uncertainty by examining its role in corporate intelligent transformation, offering actionable strategies for firms to mitigate climate risks while providing policy insights for developing economies to leverage smart technologies in addressing CPU. Full article

Current technological innovation in mega projects faces the problem of mismatch between supply and demand, where technology demand-side entities struggle to translate engineering problems into precise scientific research language, while technology supply-side entities fail to capture authentic scenario parameters from engineering sites. This study employs an evolutionary game model to thoroughly investigate behavioral interaction processes among governance entities, demand-side entities, and intermediary collaborative innovation platforms during technological innovation supply–demand matching. By constructing and deriving a tripartite evolutionary game model, this research analyzes the impacts of initial states, the matching effort coefficient, the innovation risk coefficient, and other factors on the evolution of scientific technological innovation supply–demand matching. Additionally, this study simulates the dynamic evolutionary processes of strategic selection. The findings reveal that the initial states of the three parties do not influence behavioral evolution. Furthermore, the subsidy coefficient, additional benefits, and risk coefficient emerge as the most significant parameters affecting tripartite strategy selection. The research outcomes and managerial implications provide crucial reference value for enhancing the alignment between scientific research supply and demand in mega projects, thereby promoting the transformation of scientific and technological achievements in major railway engineering projects. Full article

The digital creative industry (DCI) has become an integral part of China’s strategic emerging industries. This paper, utilizing county-level data from Chinese digital creative enterprises in 2022, examines the locational factors influencing the spatial distribution of China’s DCI through Principal Component Analysis and Multiple Linear Regression Analysis. The findings indicate that technological innovation and the level of economic development universally and dominantly influence the agglomeration of all DCI sub-sectors. Service-oriented digital creative enterprises are more likely to cluster in areas with abundant cultural resources and public facilities, with government policies and financial subsidies playing a significant role. In contrast, digital creative equipment manufacturing companies are more likely to locate in proximity to market demand and related industries. Full article