Search Results (2,885)

Low efficiency of environmental regulation (ERE) has become a major obstacle to effective environmental governance in developing countries. There is an urgent need to identify the key weak links of low ERE and explore external driving factors to improve ERE. Based on the operational mechanism of government environmental regulation, the regulatory process is divided into a supervision stage and a governance stage, and the intrinsic mechanism through which digital infrastructure affects ERE is theoretically explored. Subsequently, this paper uses a two-stage Data Envelopment Analysis (DEA) to measure the ERE in China from 2007 to 2022. On this basis, this study uses econometric models to test the theoretical mechanism. The findings are as follows: Firstly, although ERE has shown an upward trend over time, there is significant heterogeneity across regions. The increasing trend in ERE is largely attributed to improvements in governance efficiency (GE). Secondly, digital infrastructure is significantly and positively related to ERE. The relationship is mainly reflected in the governance efficiency stage. Thirdly, heterogeneity analysis shows that the positive correlation between digital infrastructure and ERE and GE is more prominent in the central–western regions and resource-based regions. The positive correlation between digital infrastructure and supervision efficiency (SE) is stronger in regions with stronger promotion incentives. In regions with close external environmental attention, the positive correlation between digital infrastructure and GE is more pronounced. However, we found no evidence that environmental assessment pressure significantly affected this relationship. Full article

This work examined, across a 20-year horizon, how much energy could be recovered from the biogas generated at UASB reactors versus an from an activated sludge process system (ASPS) treating wastewater in Sorocaba (SP, Brazil). A logistic model placed the 2046 Sorocaba population at 1,070,692 inhabitants, corresponding to a total sewage flow of 1916.9 L/s. The UASB reactor required a total volume of 36,819.8 m³ (34 units), producing up to 6524.91 m³/day of biogas and 4241.19 m³ CH₄/day, without primary settling, yielding 575.55 kW and 4285.5 MWh/year, whereas the activated sludge system showed substantially higher potential, with 21,330.75 m³/day of biogas (7,785,724 m³/year), 1112.09 kW of installed power, and 8767.70 MWh/year of electricity. These values quantify the gross energy-recovery potential of each configuration, that is, the energy embodied in the recoverable biogas and its conversion to electricity, rather than a plant-wide net-energy balance, since internal process demands, such as aeration in the activated sludge system, are not deducted. Hydrogen output was additionally evaluated by upgrading the biogas to biomethane and then applying steam methane reforming (SMR); here, the activated sludge route achieved a levelized cost of hydrogen (LCOH) of US$ 5.00/kg H₂, below the US$ 8.58/kg H₂ found for UASB. However, the economic analysis indicated infeasibility for both systems, with a negative NPV in all scenarios (−US$ 1,051,069 for UASB and −US$ 1,904,999 for ASPS, without carbon credits, improving markedly once carbon credits are included) and paybacks of 4.4–7.5 years in the distributed-generation scenario. The industrial wastewater contribution was represented using hypothetical data from a small dairy plant rather than actual discharge records from the Sorocaba system, directly influencing the estimated biogas and energy-recovery potential. Hence, even though lower greenhouse gas emissions and renewable energy use bring clear environmental gains, feasibility remains constrained by capital and operating conditions, indicating that incentive mechanisms are needed to expand the uptake of these technologies within the sanitation sector. Full article

Supply disruption management requires not only operational risk mitigation but also sustained incentives for upstream reliability investment. This study examines whether backward partial vertical ownership can improve delivery reliability when a dominant retailer procures from a manufacturer whose reliability-enhancing effort is costly. We develop a retailer-led Stackelberg model and compare a linear-procurement no-ownership benchmark with a backward-ownership structure in which the retailer obtains a proportional residual claim without direct operational control. The results show that ownership allows part of the upstream return generated by procurement support to flow back to the retailer, thereby reducing its effective support cost, increasing the procurement price, and inducing greater manufacturer reliability-enhancing effort. Within the proposed model, any positive ownership share improves reliability-enhancing effort and gross operational supply chain profit relative to linear procurement, although the retailer consistently prefers a higher share while the manufacturer prefers a unique interior share. We therefore introduce a lump-sum net transfer and Nash bargaining to implement Pareto-improving ownership arrangements. The ownership share determines reliability incentives and cooperative surplus, whereas the transfer payment satisfies participation constraints and allocates the gains; bargaining power affects surplus distribution but not delivery reliability or operational efficiency. In the frictionless model, the bargaining objective approaches its supremum as the ownership share approaches the unattained theoretical upper boundary, and manufacturer reliability-enhancing effort and total supply chain profit converge to their centralized levels. When capital frictions are introduced, positive gross operational value need not imply positive net cooperative surplus after implementation costs, and the optimal ownership share becomes interior and decreases with capital frictions. These findings identify backward partial ownership as a complementary governance mechanism for improving supply reliability relative to linear procurement and explain why minority ownership may be optimal in practice. Full article

This study examines the association between the debt tax shield (DTS) and corporate leverage in 61 listed non-financial firms from Chile, Colombia, Mexico, and Peru during 2013–2023, using a perfectly balanced panel of 671 firm-year observations obtained from Bloomberg. The DTS is measured as interest expense multiplied by the effective income tax rate and scaled by total assets. Fixed-effects and random-effects panel models are estimated and complemented by a correlated random-effects (Mundlak) specification, a liquidity-augmented model, and several robustness checks. The contemporaneous DTS is positively associated with leverage, although the magnitude and statistical significance of the coefficient are sensitive to model specification. In particular, the relationship becomes statistically insignificant when the DTS is lagged and increases substantially after winsorization, while remaining positive and statistically significant when the DTS is reconstructed using statutory corporate income tax rates and positive at marginal significance when a non-debt tax shield control is included. The Mundlak decomposition further shows that the association is driven primarily by persistent between-firm differences rather than by within-firm changes over time. These findings indicate that the DTS–leverage relationship should be interpreted as a contemporaneous association rather than as evidence of a causal financing effect. The study contributes to the limited empirical evidence on debt-related tax incentives and capital structure in Latin American listed firms and highlights the importance of distinguishing persistent cross-sectional heterogeneity from dynamic financing behavior. Full article

This study examines the relationship between public electric-vehicle charging-infrastructure deployment and the credit risk of rural commercial banks in China. Using an unbalanced panel dataset of 180 RCBs across 10 provinces from 2016 to 2022, this paper employs a two-way fixed-effects model and uses the provincial number of public charging piles as a proxy for the deployment scale of public charging infrastructure. The results show that the rapid expansion of public charging infrastructure is statistically associated with higher non-performing loan ratios among local RCBs and this association remains under alternative standard-error specifications and robustness tests. The theoretical analysis suggests that this association may be related to policy uncertainty, technological upgrading pressure, utilization uncertainty, maturity mismatch, and market competition in the new energy vehicle charging sector. Heterogeneity results indicate that the positive association is weaker in economically stronger regions, while higher provision coverage is associated with a stronger positive relationship, possibly reflecting risk-taking incentives, historical asset-quality differences, or delayed non-performing asset disposal. By connecting sustainable transport infrastructure, green finance, infrastructure governance, and local banking risk, this study contributes to debates on how low-carbon mobility transitions can be financed without weakening regional financial stability. The findings highlight the need for coordinated transport–energy planning, clearer regulatory standards, regionally differentiated risk-sharing mechanisms, and stronger financial governance frameworks for public charging infrastructure. Because the analysis relies on provincial-level infrastructure data rather than project-bank matched loan-level data, the documented findings should be interpreted as statistical associations rather than definitive causal relationships. Full article

Promoting a virtuous synergy between fiscal and financial systems serves as a pivotal policy instrument for dismantling the urban–rural dual structure and advancing common prosperity. Utilizing panel data from 277 Chinese prefecture-level cities (2011–2023), this study constructs a coupling coordination degree model to systematically gauge the level of fiscal–financial interaction. It then conducts an in-depth assessment of how this synergy impacts the urban–rural income disparity, focusing on its mechanisms, nonlinear traits, and spatial effects. The empirical validation yields four principal findings: (1) Fiscal–financial synergy significantly narrows the urban–rural income gap, a conclusion robust to a battery of tests for endogeneity and alternative specifications. (2) Mechanism analysis reveals that this convergence is driven by four channels: industrial structure upgrading effects, entrepreneurial incentives, enhanced social welfare, and clean governance effects. (3) Heterogeneity analysis indicates that the gap-narrowing effect is markedly stronger in non-central cities, areas with lower urbanization rates, weaker fiscal transparency, and less-developed markets. This exhibits pronounced inclusivity and a distinct “shortfall-remedying” characteristic. (4) Nonlinear and spatial examinations uncover a significant threshold effect: crossing a specific threshold triggers a marginal increasing effect on convergence. Furthermore, the policy generates positive spatial spillovers to adjacent regions through factor mobility. By adopting a fiscal–financial co-governance perspective, this paper enriches the empirical evidence on income inequality mitigation and offers vital policy insights for optimizing cross-departmental policy mixes and deepening urban–rural integration. Full article

Against the dual backdrop of the “dual carbon” goals and the digital transformation of the construction enterprises (CEs), green intelligent building technologies (GIBTs) have become the core driving force for improving quality and efficiency in the CEs and achieving low-carbon, sustainable development. Taking the collaborative innovation and development of GIBTs as the research context, this paper constructs an evolutionary game model involving three players (TPs), the government, CEs, and academic and research institutions (ARIs), to systematically analyze the evolution-stable strategies of the collaborative innovation system for GIBTs and their underlying mechanisms. The research findings indicate: (1) Government subsidy funds for collaborative innovation directed at both CEs and ARIs have distinct critical thresholds; the subsidy amount directly determines whether the system can converge to the ideal equilibrium of comprehensive three-player collaboration (participation, collaboration, collaboration). (2) The incentive effects of green incentives on the two types of innovation entities, CEs and ARIs, exhibit significant heterogeneity. Green incentives for CEs are highly sensitive core control parameters, while those for ARIs are weakly sensitive auxiliary parameters. (3) The cost-sharing ratios for research and development (R&D) between CEs and ARIs affect, respectively, the equilibrium level of the TPs collaboration and the stability of the system’s dynamic convergence. Relying solely on internal cost-sharing mechanisms between these two groups cannot achieve deep collaboration among TPs; it must be complemented by collaborative external policy adjustments such as government subsidy funds and green incentives. Such deep collaboration among TPs emphasizes the substantive enhancement of coordination efficiency and cooperative quality on the basis of full participation, reflecting the maturity and effectiveness of three-player innovation interaction. (4) Default penalties and the returns from CEs’ independent R&D constitute, respectively, the rigid constraint mechanism and the market-driven mechanism for collaborative innovation in GIBTs. Working in tandem, one exerting a restraining effect and the other a stimulating effect, they jointly regulate the cooperative behavior of CEs and ARIs, thereby resolving the dilemma of insufficient motivation for collaborative innovation in GIBTs. In terms of theoretical contributions, this study employs evolutionary game theory and numerical simulation methods to refine theories related to policy thresholds, differentiated incentives, collaborative governance, and the balance of benefits and risks among industry, ARIs. It also demonstrates that the underlying logic can serve as a reference for developing countries with similar industrial structures. In terms of practical contributions, this study recommends establishing subsidy allocation standards based on subsidy critical thresholds; implementing differentiated tax incentives for CEs, ARIs; scientifically designing R&D cost-sharing ratios; and improving mechanisms for penalty-based enforcement and incentives for independent R&D. Furthermore, it proposes advancing institutional development, policy implementation, and system optimization in a progressive manner, short-term, medium-term, and long-term, according to priority. Full article

With the increasing proportion of flexible resources such as distributed generation, energy storage and demand response in new power systems, load aggregators, as an important subject connecting users and the market, face the challenges of complex load types, large differences in response capabilities, and high peaking costs. Therefore, this paper proposes a coordinated optimization strategy of load and electricity consumption considering aggregator load demand and peak load regulation incentive. Firstly, based on the operating characteristics of load equipment in multiple scenarios, the aggregator load is divided into three categories: energy storage type, elastic electrical equipment and inelastic electrical equipment, and the corresponding electricity cost model is established. Combined with utility theory and user subjective perception, a residential and industrial electricity comfort model is constructed. Secondly, a price-elasticity-based load potential assessment method is developed to quantify response envelopes under time-of-use tariffs, and a bi-level optimization model considering peak load regulation incentives is constructed. Next, in order to solve the bi-level non-convex optimization problem efficiently, a reinforcement learning solution framework based on a multi-agent deep deterministic policy gradient is proposed. The aggregator and user groups are modeled as collaborative agents respectively, and the global optimal strategy is realized by centralized training and decentralized execution. Finally, simulation results show that the proposed strategy can effectively guide the load from the peak period to the trough period, significantly improve the peak load shifting effect, improve the users’ electricity satisfaction, and reduce demand-side response cost. Full article

Renewable energy transitions depend not only on capacity targets and financial incentives but also on coherent legal arrangements governing institutions, infrastructure, investment, environmental protection, and technological capability. This article develops a Five-Pillar Framework comprising Institutions and Policies, Infrastructure and Technology, Finance and Investment, Society and Environment, and Human Resources and Research and Development. It applies a comparative doctrinal and policy analysis to Vietnam and China, two state-led Asian economies with similar transition objectives but different regulatory maturity and implementation capacity. The assessment uses three cross-cutting criteria—legal certainty, institutional coherence, and implementation capacity—and is supplemented by a 2026 survey of 150 Vietnamese stakeholders from four professional groups. The findings show that China has a comparatively more consolidated statutory framework, coordinated grid planning, diversified financing instruments, and stronger domestic technological capacity, while also facing subsidy arrears, regional grid imbalances, and socio-environmental risks. Vietnam has modernized its electricity legislation, direct power purchase framework, and renewable-energy regulation, but implementation remains constrained by legislative dispersion, grid congestion, weak contractual risk allocation, and remaining limitations in the specificity and implementation of storage, social-impact, and innovation rules. China therefore offers a context-sensitive benchmark rather than a directly transferable model. The article proposes sequenced reforms for Vietnam, prioritizing regulatory continuity, bankable contracts, grid flexibility, social safeguards, legal coordination, and domestic innovation. Full article

One of the key challenges of the contemporary solar energy market is matching supply and demand. The demand side of this problem largely depends on the difficult-to-predict, individualized, numerous, and dispersed behaviors of prosumers. With this in mind, the main objective of the study is to identify the structural relationships between types of prosumer behavior and the motivators encouraging prosumers to modify their existing habits. Beyond this main objective, the study also assessed prosumers’ perception of positive and negative motivators and identified patterns of prosumer behavior regarding energy consumption following the installation of a photovoltaic system. These objectives were pursued through a questionnaire-based survey conducted on a representative sample of 754 Polish prosumers. The analysis of the results employed descriptive statistics, correlation analysis, and structural equation modeling. The results clearly show that positive incentives—rather than orders and penalties—are more strongly associated with the modification of energy consumption patterns. Prosumers place equally high value on increasing energy independence and on financial savings. The surveyed prosumers predominantly declare a responsible and adaptive attitude toward solar energy consumption, manifested in monitoring their consumption and adjusting their behavioral patterns toward maintaining or reducing overall energy use. Fewer prosumers report the rebound effect or passive consumption patterns than report adaptive or responsible behavior. Only positive motivators (cost reduction, increased independence, and participation in climate protection) prove useful in modifying existing consumption patterns, with responsible and adaptive prosumers showing the greatest potential for change under their influence. Negative motivators, in the form of penalties or the absence of incentives, are not significantly associated with any of the identified prosumer attitudes. The results provide practical implications for energy policy design in developing and emerging economies. Full article

Agricultural science and technology innovation is an important foundation for ensuring food security and promoting sustainable agricultural development. This paper constructs a multi-task principal-agent model between the government and agricultural research institutions to explore how to optimize public R&D incentive mechanisms under conditions of goal conflict and information asymmetry, so as to guide research institutions in allocating effort appropriately between academic-oriented tasks and industry-oriented tasks. The study combines theoretical modeling, case analysis, and numerical simulation: the theoretical model is used to identify the conditions for optimal incentives, the case analysis is used to illustrate how incentive structure imbalances manifest in practice, and the numerical simulation is used to demonstrate the possible effects of incentive correction paths. The theoretical analysis shows that the optimal incentive intensity for a task is positively related to the marginal benefit of its output and negatively related to output uncertainty and the cost coefficient. The effects of the risk aversion coefficient and task substitutability on incentive intensity exhibit threshold conditions, and the direction of these effects depends on the relative levels of the marginal benefits of the two types of tasks. Taking the Jiangsu Academy of Agricultural Sciences as an example, the case analysis finds that academic-oriented tasks receive higher incentives because of their short-term observability, while industry-oriented tasks are under-incentivized due to their long cycles, high risks, and high costs. The numerical simulation further indicates that when the output uncertainty of industry-oriented tasks decreases, the cost coefficient declines, and the marginal benefit increases; the imbalance in incentive allocation may be improved within the scope of the model setting. These conclusions provide policy references for optimizing agricultural research incentive mechanisms and promoting the contribution of agricultural science and technology to sustainable agriculture. Full article

Agricultural green transformation is essential for high-quality agricultural development and rural modernization, and farmers’ adoption of green production technologies is central to this process. Although many studies have examined such adoption, limited attention has been paid to farmland transfer contract stability. Using survey data from 1211 farm households in Guizhou Province, China, this study examines how farmland transfer contract stability affects farmers’ adoption of green production technologies. Logit and negative binomial models estimate adoption decisions and adoption intensity, while the control function approach and propensity score matching address endogeneity and selection bias. The results show that contract stability significantly increases both the likelihood and intensity of green production technology adoption, with renewal intention exerting the strongest effect. Mechanism analysis indicates that contract stability works mainly by enhancing farmers’ perceived stability of long-term green returns and promoting mechanization investment/service use. Partly substitute for the effect of contract stability on adoption intensity. The effect on adoption intensity is strongest among medium-scale farmers. Cooperatives weaken the effect of transfer duration but strengthen that of renewal intention, while science and technology commissioners complement contract stability, particularly for written contracts and renewal intention. These findings suggest that improving farmland transfer contract systems and coordinating land institutions with green incentive policies can advance agricultural green transformation. Full article

In the agricultural production cycle, farmers face financial constraints both before planting and before harvesting, which can substantially restrict production decisions and operational efficiency. With the rapid development of agricultural e-commerce and supply chain finance, platform loans and pre-sale financing have become important channels through which farmers can obtain the funds needed for agricultural production. This study develops a two-stage financing portfolio decision model for an agricultural product supply chain and examines the farmer’s optimal financing strategy, planting quantity, and pricing decisions under exogenously given platform loan and pre-sale financing conditions. The results show that the loan interest rate and commission rate significantly influence the farmer’s choice of financing strategy. Specifically, holding other conditions constant, a higher loan interest rate increases the farmer’s incentive to adopt pre-sale financing, whereas a higher commission rate reduces the incentive to adopt it. Numerical simulations further identify the decision boundaries of the two-stage financing portfolio strategies under different parameter conditions. This study provides theoretical insights into farmers’ financing decisions and the selection of multi-stage financing strategies in agricultural product supply chains. Full article

Freshwater fisheries are increasingly threatened by environmental pollution and growing market demand, requiring management strategies that balance ecological sustainability with economic returns. We develop a bioeconomic fishery model for a polluted lake that integrates fish population dynamics, environmental pollution, adaptive harvesting effort, endogenous fish price, pollution-control investment, and policy-regulated harvesting-cost control within a unified ecological-economic framework. The model exhibits multiple stable states, including sustainable harvesting, harvesting-free, and fish-extinction regimes. A critical pollution-input threshold is identified beyond which the fish-extinction equilibrium becomes locally stable, indicating an increased risk of population collapse. Increasing pollution induces bistability, whereas sufficiently high market demand destabilizes the coexistence equilibrium through a Hopf bifurcation, producing persistent oscillations. The interaction between pollution and market demand further generates hysteresis, implying that restoring degraded fisheries requires substantially stronger interventions than preventing collapse. Treating the policy-regulated harvesting cost as the control variable, we derive an optimal harvesting policy consisting of a bang-bang phase followed by a singular control that maximizes the long-term harvesting revenue. Overall, our results demonstrate that coupled ecological-economic feedbacks can generate tipping points, oscillatory dynamics, and path-dependent transitions, highlighting the importance of integrated pollution control, harvesting regulation, and economic incentives for sustainable fishery management. Full article

Existing contract-net-based UAV scheduling methods commonly use static or event-switched objective weights and provide limited evidence on how state-responsive scoring behaves under failures and communication impairment. This paper proposes a Dynamic Weight Multi-Objective Algorithm (DW-MOA) within a manager-mediated contract-net framework. Five candidate-level objectives are converted into normalized benefit utilities and combined using non-negative state-dependent weights. An independent urgency-gated spatial incentive supports cross-region dispatch without introducing negative objective weights. The method is evaluated against Traditional-CNP, Static-MOA, and HCNP-2022-adapted through matched dynamic scenarios, together with ablation, sensitivity, normalization-stability, communication-impairment, and scalability analyses. DW-MOA improves temporal performance and completed-load balance relative to Traditional-CNP, while comparisons with the stronger baselines reveal trade-offs among response time, normalized energy expenditure, flight distance, and load balance rather than uniform superiority. The mechanism remains computationally tractable at the largest tested scale and shows measurable degradation under modeled auction delay and candidate-bid loss. These findings support DW-MOA as an interpretable state-responsive scheduling method for normalized multi-UAV simulations, while physical energy calibration, continuous wireless-channel modeling, and hardware validation remain subjects for future work. Full article