Search Results (62)

To achieve dual-carbon goals and advance the sustainable development of the automotive industry, China’s Dual-Credit Policy serves as the core long-term mechanism for the low-carbon transition of the automotive industry. Given the coexistence of fuel vehicles (FVs) and new energy vehicles (NEVs) in China, existing research often overemphasizes production output while neglecting energy consumption control, and focuses predominantly on NEVs at the expense of FV optimization. To address these gaps, this paper treats FV fuel consumption and NEV energy efficiency as core endogenous decision variables. We construct profit-maximizing optimal production decision models for both types of manufacturers under the Dual-Credit Policy. Through mathematical derivation, numerical simulations, and empirical tests using actual industrial parameters, this study verifies the existence and uniqueness of optimal solutions. It clarifies the influence mechanisms of policy and market factors on corporate energy decisions and identifies the rules of strategy dominance. The findings reveal that the optimal fuel consumption decisions of FV manufacturers exhibit distinct piecewise patterns and critical threshold effects. Specifically, credit prices, NEV quotas, and fuel consumption standards determine the dominance of compliant (low-consumption) versus non-compliant (high-consumption) strategies. Furthermore, the policy exerts a significant market-oriented positive incentive on the energy efficiency upgrading of NEV manufacturers, with credit prices, market demand, and R&D costs acting as core constraints. Notably, the transition-guiding effect of the policy has clear effective boundaries, and its efficacy highly depends on the alignment between parameter design and market conditions. This research provides theoretical support for manufacturers to formulate energy-optimized production decisions and offers actionable references for the continuous optimization of the Dual-Credit Policy system and the sustainable low-carbon transformation of China’s automotive sector. Full article

Life Cycle Assessment (LCA) is extensively employed to support sustainability evaluation in waste management and manufacturing systems; however, outcomes are highly sensitive to methodological decisions, particularly end-of-life (EoL) allocation approaches. This study examines how cut-off and substitution approaches affect the energy performance and decarbonisation potential of high-density polyethylene (HDPE) injection moulding systems. A dual framework is adopted: first, a literature review examines methodological sensitivities in EoL modelling; second, a quantitative case study assesses industrial-scale primary data for the production of durable HDPE bottles (300 mL). The LCA model integrates specific technical parameters, including a 220 °C melt temperature and a 36 s cycle time, ensuring a realistic representation of manufacturing conditions. The results indicate that allocation choices significantly influence calculated impacts, sometimes reversing the relative ranking of configurations. Substitution-based approaches report higher benefits by crediting avoided primary production, while cut-off logic provides more conservative estimates. Quantitative analysis shows that transitioning from open-loop to fully closed-loop configurations reduces cumulative energy demand by 3.2% and freshwater emissions per functional unit by 2.8%. Furthermore, the study identifies a ‘landfill paradox’ specific to HDPE waste within transitional energy systems: due to the carbon sequestration effect of landfilled polymers and current grid emission factors, landfilling exhibits a lower net carbon footprint (0.03 kg CO₂-eq./kg) than high-efficiency incineration (1.54 kg CO₂-eq./kg). These findings highlight that circular economy evaluations are strongly shaped by methodological assumptions, with direct implications for energy policy. Bridging the gap between specific industrial processing parameters and end-of-life allocation logic underscores the need to incorporate primary industrial data and transparent allocation frameworks to support reliable decision-making in the transition toward low-carbon and energy-efficient manufacturing systems. Full article

This study examines the impact of Sustainable Livelihood Capital (SLC) on women’s participation in production activities in Vietnam’s mangrove areas, using a gender-focused approach to advance gender-sensitive livelihood theory. Employing binary Logit regression and cross-tabulation analysis, model robustness was confirmed via the Bayesian Information Criterion (BIC). Findings identify financial and human capital as core factors influencing participation, with access to credit emerging as the factor exhibiting the strongest correlation. Livelihood stability and practical vocational training support women’s long-term productive engagement. The study highlights the livelihood paradox and resource lock-in effects: over-reliance on mangrove income reduces participation, limiting diversification, while over-exploitation correlates positively with participation as a survival strategy, exerting short-term environmental pressure. Conversely, owning traditional assets like fishing boats negatively affects participation, showing traditional factors hinder economic restructuring. The findings of heterogeneity analysis emphasize the necessity of policy intervention. For example, women under 56 are primarily financially driven; those over 56 exhibit lower participation and face severe human capital bottlenecks, especially education. Larger households face significant financial barriers despite having abundant human capital. Married women face dual constraints from financial and traditional physical capital, while single/divorced women hold advantages in education and opportunities. Furthermore, In areas far from fishing ports, financial and human capital are core drivers. This research provides quantitative evidence on the complex, heterogeneous effects of SLC on women’s productive engagement, offering a scientific foundation for multi-dimensional, targeted policy measures to foster sustainable livelihood diversification. Full article

Green finance has emerged as a crucial instrument for driving the macroeconomic transition toward a low-carbon economy, yet its specific transmission mechanisms warrant deeper empirical scrutiny. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this scientific study employs a staggered difference-in-differences (DID) framework using provincial panel data from 2009 to 2023. To overcome the limitations of unidimensional metrics, we developed a comprehensive Industrial Structure Upgrading Index (ISUI) that integrates structural rationalization, advancement, and greening. The empirical findings reveal that the green finance pilot policy exerts a significant and positive impact on the ISUI. This core result remains robust under a series of rigorous checks, including the Callaway and Sant’Anna (CS-DID) estimator. Mechanism analyses demonstrate a dual “push–pull” dynamic: Green Credit Intensity (GCI) acts as the primary mediating channel by directing targeted financial resources (financial pull), while stringent environmental regulation positively moderates this effect (administrative push). Furthermore, the moderating role of digital finance is statistically non-significant, underscoring the policy’s broad inclusiveness and its independence from regional digital infrastructure. Heterogeneity estimations identify a clear structural catch-up effect, with more pronounced benefits observed in resource-dependent regions and areas with historically lower innovation capacities. Ultimately, these findings indicate that coordinating targeted financial incentives with environmental oversight can effectively drive multidimensional industrial upgrading, providing valuable evidence for sustainable transition strategies. Full article

Achieving simultaneous control over total carbon emissions and intensity is essential for China’s dual carbon goals. Using panel data from 1235 listed manufacturing firms (2015–2022), we construct a composite index to measure dual carbon control and investigate how data elements influence corporate carbon performance from an industry heterogeneity perspective. The main findings are as follows. (1) Data elements significantly enhance dual carbon control, with effects concentrated in high-pollution sectors, particularly metallurgy and mineral products, while remaining insignificant in low-pollution industries. (2) Mechanisms differ across industry types: capacity utilization drives improvements in high-pollution industries, whereas green technology innovation matters in low-pollution sectors such as agro-processing and textiles. (3) ESG disclosure and green credit subsidies amplify these effects, though with varying efficacy. Policymakers should adopt differentiated strategies including removing structural barriers to green innovation in high-pollution industries and activating capacity utilization through monitoring standards and technology markets in low-pollution sectors. A tailored policy framework is essential to realize the full potential of data elements in advancing China’s dual carbon goals. Full article

This paper focuses on alternatives to the CAFC-NEV credits policy in the automotive industry of China. It considers a dual-channel supply chain consisting of a manufacturer and a retailer that can simultaneously produce and sell new energy vehicles (NEVs) and internal combustion engine vehicles (ICEVs). Differential game theory is employed to explore dynamic optimal decisions under CAFC-NEV credits and carbon credit policies. The results suggest that the strategies combining CAFC-NEV credits and carbon credit policies are equivalent to a single CAFC-NEV credits policy. Therefore, implementing the carbon credit policy on the basis of the CAFC-NEV credits policy does not affect the increase in NEV range. If the NEV credit score is below a certain threshold, the carbon credit policy will result in a higher range increase and brand goodwill of NEV. In the transition process of implementing the carbon credit policy based on CAFC-NEV credits and subsequently canceling the CAFC-NEV credit policy, the profits of supply chain members change slightly. The findings provide a theoretical basis for the timely exit of the CAFC-NEV credits policy. Full article

The development of new energy technologies is crucial for the future competitiveness of the automotive industry. Green innovation is a key driver of industrial transformation and advancement. Companies in the new energy vehicle (NEV) sector play a critical role in the automotive supply chain and demonstrate their green innovation capabilities across the industry. The dual-credit policy, a major governmental regulatory incentive, has a significant impact on the innovation performance of NEVs. Therefore, it is important to examine its influence on green innovation outcomes. This study is grounded in institutional theory and the resource-based view, and informed by the TOE analytical framework. It aims to develop a theoretical model to investigate the interplay among technological, organizational, and environmental factors in fostering green innovation. Using panel data from 21 NEV companies spanning the period 2014–2023, the research employs the dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) method to identify causal configurations associated with high green innovation performance. The results show that no single factor is necessary for achieving superior outcomes. Configuration analysis reveals 3 dominant pathways: “Technology-driven + Environment-pulled” pathway, “Technology-driven + organizational collaboration” pathway and the “Tripartite linkage” pathway. This study advances theoretical understanding by moving beyond unidimensional analyses and offering a holistic perspective on the multiple equifinal paths to high green innovation performance. It also provides practical insights for NEV firms to strategically align their technological, organizational, and environmental resources to enhance green innovation performance. Full article

This study examines the systemic operational risk in Morocco’s banking sector using a Panel VAR model based on data from three banks over ten years. The model includes real GDP, interbank rate (TMP), and bank credit, alongside indicators of operational, credit, and liquidity risks. The Impulse Response Functions (IRF) show that operational risk shocks reduce GDP and affect TMP with a lag, confirming their systemic impact. Forecast Error Variance Decomposition (FEVD) reveals that GDP significantly explains the variance in operational risk. To strengthen the analysis, a dynamic panel GMM model is used to address endogeneity. The GMM results demonstrate that systemic operational risk in Moroccan banks is both persistent and procyclical, highlighting how macro-financial dynamics such as growth, inflation, and monetary conditions, directly shape banks’ resilience. These findings provide new empirical evidence on the determinants of systemic operational risk in emerging markets. This dual approach supports the integration of operational risk into Morocco’s macroprudential policy frameworks. Full article

Driven by carbon neutrality policies, the cumulative issuance volume of the global green bond market has surpassed $2.5 trillion over the past five years, with China, as the second largest issuer, accounting for 15%. However, there exists a yield difference of up to 0.8% for bonds with the same credit rating across different policy regions, and the premium level fluctuates dramatically with market cycles, severely restricting the efficiency of green resource allocation. This study innovatively constructs a Bayesian hierarchical spatiotemporal model framework to systematically analyze pricing deviations through a three-level data structure: the base level quantifies the impact of bond micro-characteristics (third-party certification reduces financing costs by 0.15%), the temporal level captures market dynamics using autoregressive processes (premium volatility increases by 50% during economic recessions), and the spatial level reveals policy regional dependencies using conditional autoregressive models (carbon trading pilot provinces and cities form premium sinkholes). The core breakthroughs are: 1. Designing spatiotemporal interaction terms to explicitly model the policy diffusion process, with empirical evidence showing that the green finance reform pilot zone policy has a radiation radius of 200 km within three years, leading to a 0.10% increase in premiums in neighboring provinces; 2. Quantifying the posterior distribution of parameters using the Markov Chain Monte Carlo algorithm, demonstrating that the posterior mean of the policy effect in pilot provinces is −0.211%, with a half-life of 0.75 years, and the residual effect in non-pilot provinces is only −0.042%; 3. Establishing a hierarchical shrinkage prior mechanism, which reduces prediction error by 41% compared to traditional models in out-of-sample testing. Key findings include: the contribution of policy pilots is −0.192%, surpassing the effect of issuer credit ratings, and a 10 yuan/ton increase in carbon price can sustainably reduce premiums by 0.117%. In 2021, the “dual carbon” policy contributed 32% to premium changes through spatiotemporal interaction channels. The research results provide quantitative tools for issuers to optimize financing timing, investors to identify cross-regional arbitrage, and regulators to assess policy coordination, promoting the transformation of the green bond market from an efficiency priority to equitable allocation paradigm. Full article

Currently, corporate innovation has become a key driver of economic growth and a critical factor in enhancing core competitiveness, which is of great significance for achieving sustainable economic development. Our research is based on panel data from A-share-listed manufacturing companies in China between 2012 and 2022, employing a multi-time point Difference-in-Differences (DID) model and a DID model extended with the Dual Machine Learning (DML) estimation method for empirical testing. We investigate the underlying mechanisms and analyze corporate heterogeneity. The findings reveal that the pilot policy of industry–finance collaboration has a significant positive impact on corporate innovation, particularly for companies facing severe financing constraints, intense market competition, and relatively small scales. Additionally, the study finds that the pilot policy promotes corporate innovation through three channels: reducing information asymmetry, increasing local government fiscal subsidies, and enhancing corporate access to bank credit. Finally, we provide recommendations for the government, enterprises, and financial institutions to further leverage and enhance the effectiveness of the industry–finance collaboration pilot policy in boosting corporate innovation. Full article

Achieving Sustainable Development Goals (SDGs) requires integrated interventions that leverage synergies and minimize trade-offs across sectors and institutions. However, siloed institutional structures often prevent such alignment. Using panel data from 361 households and a difference-in-differences approach, this study examines how an integrated landscape restoration intervention, combining homestead gardening, soil and water conservation (SWC), and credit provision, affects SDG outcomes in rural Ethiopia. The study evaluated impacts on SDG-1 (no poverty), SDG-2 (zero-hunger), SDG-13 (climate-action), and SDG-15 (life-on-land) outcomes. Results indicate no statistically significant outcomes from single-intervention participation. Among dual interventions, SWC + credit improved all SDG indicators except SDG-1, while homestead gardening + SWC showed limited impacts. These results suggest that credit provision plays a critical catalyst in widening the impact of biophysical interventions across multiple SDGs. Participation in the full tripartite intervention induced significant, synergistic improvements across all SDG outcomes. These findings provide empirical evidence that bundling biophysical restoration with socio-economic interventions maximizes synergies. The results also underscore the need to inform integrated development approaches using ex-ante analysis of potential synergies and trade-offs among interventions to optimize efficacy and avoid unintended consequences. The findings offer critical guidance for evidence-based multi-objective policy formulation to advance the 2030 Agenda. Full article

The purpose of this study was to assess workers’ perceptions, experiences, and strategies related to financial health, with the goal of identifying and validating a model of financial resilience aligned with theoretical and empirical fit criteria. A sequential quantitative approach was employed, combining exploratory factor analysis (EFA) to uncover latent dimensions, followed by confirmatory factor analysis (CFA) to validate the resulting structure. This dual methodology was designed to ensure both empirical robustness and theoretical coherence. The study used a non-experimental, cross-sectional design and drew on survey data from 311 employees of a sugar company in San Juan Bautista Tuxtepec, Oaxaca, selected through a non-probabilistic self-selection sampling method. The instrument, based on existing models was administered electronically. Internal consistency was assessed using Cronbach’s alpha (α), McDonald’s omega (ω), composite reliability (CR), and average variance extracted (AVE), while multivariate normality was also examined. Findings reveal that financial resilience encompasses not only recovery from financial shocks but also proactive financial behaviors such as budgeting, long-term saving, and responsible debt management. Respondents emphasized the role of credit history, insurance access, and perceived financial autonomy in promoting both financial stability and emotional well-being. These results contribute to the theoretical conceptualization of financial resilience and have practical implications for policy and financial education with a preventive and mental health-oriented perspective. Full article

Against the backdrop of the dual carbon goals, the construction industry—as the primary source of carbon emissions accounting for 50.9%—is increasingly relying on green technological innovation to drive its sustainable development transformation. However, construction enterprises currently face three core challenges: the significant incremental costs associated with adopting green technologies, insufficient green credit supply from financial institutions, especially banks, and inadequate policy coordination among government departments. Furthermore, misaligned interests among multiple stakeholders exacerbate the implementation challenges of green technological innovation, hindering the industry′s low-carbon transition. Therefore, systematically exploring the interaction patterns and functional mechanisms among construction enterprises, government agencies, and banks in green technology innovation decision-making is crucial. This study will provide theoretical and empirical support for the green transformation of the construction industry within the dual-carbon framework. This study establishes a tripartite game model involving construction companies, governments, and banks, centered around the decision-making phase of green technology innovation. By integrating evolutionary game theory with system dynamics (SD) approaches, it uncovers the evolutionary trajectories and underlying mechanisms of strategies adopted by each stakeholder. Research indicates that construction companies, governments, and banks ultimately maintain equilibrium at the (1,1,1) point. The study underscores the pivotal role of government guidance during the decision-making stage, highlighting that sustained implementation of proactive policies can foster positive interactions and a balance between construction companies’ pursuit of green technology innovation and banks’ provision of green credit. It can shorten the time required for enterprises and banks to evolve their strategies. Suppressing the probability of innovation failure moderates both parties′ strategies, and adjusting parameters such as green credit interest rates and government subsidies can optimize choices. This research not only enhances the theoretical understanding of green technology innovation in the construction sector but also offers practical insights for promoting industry-wide green innovation, improving the quality of green buildings, and regulating market order. Full article

In recent years, China’s economic and social development has faced challenges such as urban-rural imbalance and ecological pressure. Digital inclusive finance and ecological resilience have become key concerns in academia and policymaking. This study empirically examines whether digital inclusive finance can enhance ecological resilience and its underlying mechanisms, drawing on quantitative evidence from provincial panel data covering 2011–2020. By providing robust empirical results, it contributes to understanding the role of digital finance in supporting high-quality growth and ecological civilization. While the findings align with national strategies such as the “dual carbon” goal and rural revitalization, the study’s primary contribution lies in advancing interdisciplinary exploration through rigorous evidence rather than solely at the policy level. By constructing a double fixed effects model and panel data from 30 Chinese provinces (2011–2020), the study finds that digital inclusive finance significantly enhances ecological resilience, both directly and indirectly through channels such as environmental regulation, artificial intelligence development, and green credit. Moreover, its ecological impact is moderated by regional economic levels and digital infrastructure, with stronger effects observed in eastern and digitally advanced regions. In summary, this study reveals the mechanisms through which digital inclusive finance promotes ecological resilience, offering a theoretical foundation and practical guidance for policy formulation. Its key contribution lies in systematically analyzing the link between digital inclusive finance and ecological resilience, enriching the theoretical framework and providing data support for policy optimization and financial institutions’ strategic adjustments. Future efforts should focus on strengthening policy coordination to enhance the ecological role of digital finance, promoting financial innovation to support resilience, and advancing regional coordination to narrow the digital divide and achieve shared ecological protection. Full article

In the severe context of global warming and the energy crisis, the low-carbon economy has become an inevitable trend in global development. This paper focuses on the logistics industry, a significant domain of carbon emissions, and regards the promotion of new energy logistics vehicles as a crucial breakthrough for the industry to achieve energy savings and emission reductions. From the perspective of an evolutionary game involving the government, logistics vehicle enterprises, and logistics enterprises, a practical and feasible strategy for promoting new energy logistics vehicles is proposed. Firstly, a tripartite evolutionary game model was developed under the dual-credit policy and auxiliary policies, and its strategy of asymptotic stability and Jacobian matrix analysis was conducted. Then, system dynamics (SD) was employed to simulate the model, aiming to explore the impact of key decision variables on the evolutionary outcomes. The results show that: (1) Appropriate auxiliary policy support can encourage logistics vehicle enterprises to produce new energy logistics vehicles and promote the transformation of the logistics industry to a low-carbon direction; (2) Through the optimization of the dual-credit policy and the enhancement of the value of points trading, logistics enterprises can be motivated to produce more new energy vehicles; (3) The promotion of cost reduction of new energy logistics vehicles and the enhancing of market competitiveness can improve the willingness of logistics enterprises to use new energy logistics vehicles; (4) The government should encourage logistics enterprises to use new energy logistics vehicles in multiple dimensions. Full article