Search Results (2,266)

The rapid expansion of China’s livestock husbandry has boosted the supply of meat, eggs, and dairy products, while concurrently giving rise to environmental pollution issues. Research on the effects of various environmental regulations on the green total factor productivity (GTFP) of the livestock sector and their underlying mechanisms is still lacking, despite the Chinese government’s implementation of corresponding environmental regulatory policies to address this practical challenge. As a key instrument for fostering green economic transformation, examining the relationship between environmental regulation and the green total factor productivity (GTFP) of animal husbandry is crucial for the sector’s sustainable development. In order to estimate the GTFP of China’s livestock sector for the years 2010–2022, this study uses the super-slack-based measure (Super-SBM) methodology. It conducts an empirical analysis to examine the mechanisms through which different environmental regulations influence livestock GTFP, alongside an investigation of regional heterogeneity. The results show that different environmental regulations have different effects on animal husbandry GTFP, with notable regional differences. Specifically, incentive-based environmental regulations enhance livestock GTFP by facilitating technological innovation; however, the level of regional economic development negatively moderates the association between incentive-based environmental regulations and livestock GTFP. The findings confirm that incentive-based environmental regulations are successful in encouraging livestock GTFP through technical innovation. They further emphasize that regions should formulate context-specific environmental regulatory policies to balance environmental protection and industrial development, thereby supporting the green and sustainable growth of China’s livestock industry. Full article

Infrastructure development is a key pillar in realising the Sustainable Development Goals. Yet implementing sustainable practices across the various stages of infrastructure development remains suboptimal. This study aims to identify significant barriers to sustainability implementation in infrastructure projects in Zimbabwe and to develop targeted interventions to overcome them. A quantitative research approach was adopted, in which 246 structured questionnaires were distributed online to construction professionals in consultancy firms, contractors, and government and private property developers in Zimbabwe. The data were analysed through a multi-analytical approach using mean score, exploratory factor analysis (EFA), and fuzzy synthetic evaluation. This study identified 31 barriers that hinder the implementation of sustainable construction in infrastructure projects. The top five factors are resistance to change, lack of funding, lack of sustainable construction policies, inadequate building regulations, and the perceived high cost of sustainable projects. EFA revealed five dimensions that are ranked as follows: ‘enforcement and policy-related’, ‘government support, regulations and standards-related’, ‘financial, market and attitude-related’, ‘knowledge, skill and ability-related’, and ‘technical capacity’. All dimensions tend to have a high level of impact on the implementation of sustainable practices in Zimbabwean infrastructure projects. The results highlight the need to enhance awareness and provide adequate financial information on the economic benefits of investing in sustainable infrastructure projects. The provision of financial incentives, funding initiatives, and appropriate policies, regulations, and standards can help to enhance the implementation of sustainable practices in Zimbabwe. Construction stakeholders can utilise the results of this study to improve the implementation of sustainability across infrastructure projects. Full article

Binary-cycle geothermal plants are inherently limited by thermodynamics, forcing operators to reinject fluids at temperatures that are still valuable for direct heating. This process results in substantial exergetic waste. While prior research has examined efficiency at the level of individual plants, this study introduces a regional-scale framework to convert these facilities into multi-purpose energy hubs. The research focuses on Türkiye’s Western Anatolia Graben, a region with high geothermal activity that, paradoxically, remains dependent on fossil fuels. By combining meteorological records with operational plant data, we evaluated the existing housing stock of 983,277 residences across 14 districts and modeled the heating requirements for a targeted capacity of 468,719 residences that the proposed system can serve. The results indicate that the currently wasted thermal load in 10 specific districts, including key centers such as Sarayköy and Alaşehir, is sufficient to cover peak winter heating demands without fossil fuel backup. Although the infrastructure requires a significant initial investment of $4.51 billion, the project demonstrates long-term viability with a Levelized Cost of Heat (LCOH) of 62.94 USD/MWh and a payback period of 10.43 years. Beyond economic considerations, the system serves as a major decarbonization tool, capable of cutting residential CO₂ emissions by 1.7 million tons annually (a 47.7% reduction). These findings suggest that policy incentives should move away from electricity-only models toward integrated reservoir management to maximize resource efficiency. Full article

Since the start of the U.S. Conservation Reserve Program (CRP) in 1985, producers have enrolled environmentally sensitive land in exchange for annual rental payments, supporting multiple dimensions of sustainability through reduced soil loss, improved water quality, enhanced habitat provision, and strengthened climate resilience through land stewardship. Recent declines in enrollment raise concerns about whether participation remains spatially aligned with local environmental need and economic incentives. This study examines regional variation in CRP participation and its sustainability implications by identifying spatial patterns in participation and key drivers using exploratory spatial data analysis (ESDA). We analyze county-level CRP participation rates alongside three key drivers (CRP rental rates, soil erosion risk on cultivated cropland, and farm income) and assess spatial dependence using Global Moran’s I, univariate Local Indicators of Spatial Association (LISA), and bivariate LISA (BiLISA). Framed as an assessment of agri-environmental policy effectiveness for sustainable land management, the framework is applied to counties in the U.S. Midwest, a region with historically substantial CRP enrollment. Global Moran’s I statistics indicate significant positive spatial autocorrelation for CRP participation (I = 0.491), CRP rental rates (I = 0.892), and soil erosion (I = 0.503), confirming pronounced regional clustering across Midwestern counties. LISA results further show that more than 60% of counties fall into high–high (HH) or low–low (LL) clusters for CRP rental rates, while BiLISA results indicate that 22.9% of counties form HH clusters between CRP participation and soil erosion, suggesting only partial alignment between CRP participation and the environmental need. These findings indicate that the environmental benefits of CRP may vary across the region depending on where participation occurs. Overall, the findings support a shift toward a data-driven, spatially explicit CRP strategy that integrates environmental risk, economic incentives, and regional context to strengthen sustainability outcomes and enhance environmental effectiveness, economic efficiency, and the spatial equity of conservation benefits in the United States. Full article

Latin America faces growing challenges in the management of municipal solid waste (MSW). This is particularly evident in medium-sized and metropolitan cities where rapid urbanization, limited infrastructure, and high proportions of organic waste (40–70%) converge. This review synthesizes the most recent advances in organic waste management, valorization strategies, environmental performance, and policy frameworks in Mexico and Latin America. To provide a comprehensive overview, evidence from studies on informal recycling systems, route optimization, sustainable landfill siting, food waste valorization, life cycle assessments (LCAs), and biogas production is integrated. Techno-economic analyses of energy recovery from organic fractions are specifically reviewed. This review highlights that valorization of organic waste through composting, anaerobic digestion, food supplementation, and bioproduct generation can reduce greenhouse gas emissions by 40–70% compared to landfilling, with AD–composting hybrids achieving the highest reductions of 60–70%. Community composting achieved moderate reductions, 30–50%, but at significantly lower cost and with greater social co-benefits. These alternatives for valorizing the organic fraction extend the lifespan of both confined and open landfills. It also contributes to mitigating the public health impacts related to open dumping, disease vectors, and contaminated leachate. In short, this review also highlights shortcomings in policy coherence, financial mechanisms, source separation, and technology adoption. A strategic framework is proposed that prioritizes decentralized treatment systems, the integration of informal recyclers, tax incentives, community-based waste separation, and planning based on Life Cycle Assessment (LCA). The findings point to a viable strategy for transitioning from landfill dependency to circular waste management systems that improve the quality of life for the population of Latin America and the Caribbean. Full article

The economic viability of livestock manure as a green agricultural input is a key determinant of growers’ adoption decisions, yet evidence on its income effects across cropping types remains limited. This study uses field survey data from growers in Jiangsu Province, China, and employs a Propensity Score Matching (PSM) method to construct a counterfactual analysis framework. We assess the impact of manure application on farm income using two core indicators: income per mu (a Chinese unit of area, approximately 0.067 hectares) and total household agricultural income. The findings demonstrate that: (1) Livestock manure application significantly increases both average income and overall household agricultural income. Cooperative membership and access to technical training significantly promote the adoption of livestock manure. (2) The income effect exhibits significant heterogeneity across crop types: vegetable and fruit growers benefit most, followed by seedling growers, while grain growers gain relatively less. By systematically comparing three cropping types, this study extends the single-crop-focused literature and provides empirical evidence for designing targeted, crop-specific policies. These findings inform the differentiated fiscal incentives and cooperative-based extension strategies proposed in this paper to enhance manure utilization efficiency in sustainable agriculture. Full article

This study investigates whether Responsible Property Investing (RPI) attributes are capitalized into condominium prices in the Bangkok Metropolitan Region. An integrated analytical framework combining Exploratory Factor Analysis (EFA) and a log–log Hedonic Price Model (HPM) was applied to a dataset of 187 condominium units derived from Environmental Impact Assessment (EIA) reports and market data. The results indicate that traditional determinants remain dominant. Unit characteristics, particularly spatial quality (β = 0.530) and interior decoration (β = 0.244), exhibit the strongest positive effects, while building amenities also contribute positively (β = 0.260). In contrast, building density (β = −0.168) and location-related distances, including transport accessibility (β = −0.323), negatively affect prices. Most RPI-related attributes are not statistically significant. Only sustainable technology (R4) shows a significant but negative effect (β = −0.206), reflecting heterogeneous valuation. These findings suggest that sustainability features are valued primarily when their benefits are directly observable, while other attributes remain weakly perceived due to information asymmetry and delayed economic returns. Overall, sustainability is only partially capitalized and context-specific in this emerging market, highlighting the need for improved market signaling, policy incentives, and greater transparency of performance information to enhance value recognition. Full article

Water governance faces growing challenges from climate change, pollution, and increasing demand, rendering policy evolution a critical research focus. This study analyzes the evolutionary characteristics of China’s national water resources governance policies from 1988 to 2025 through an integrated quantitative textual analysis. Based on 154 authoritative policy documents, the study employs Latent Dirichlet Allocation topic modeling, semantic network analysis, and a tripartite policy instrument coding scheme (command-and-control, market-based, and public participation instruments). The results reveal three key findings: a significant shift in policy attention from early administrative control toward system-oriented governance emphasizing watershed/ecological protection, conservation, and technology; a persistently imbalanced instrument mix with command-and-control tools remaining dominant, despite gradual diversification after 2000; and a three-stage evolutionary trajectory from administrative framework building (1988–1999), through comprehensive management and diversification (2000–2015), to collaborative innovation and basin/ecology integration (2016–2025). This study contributes a long-term empirical perspective on water policy evolution in an emerging economy, demonstrates an integrated textual-analytic approach, and provides actionable insights for optimizing policy mixes through strengthened incentive compatibility, substantive participation mechanisms, and coherent governance-aligned instrument portfolios. Full article

This study examines why currency substitution proves so difficult to reverse, even after countries succeed in stabilizing inflation. Focusing on Bolivia, Brazil, Mexico, and Turkey—economies that endured severe inflationary episodes before implementing stabilization programs—the paper asks a simple but important question: why does reliance on foreign currency persist long after inflation has been brought down? To answer this, the analysis adopts a structural time-series state-space framework that allows behavioral parameters to evolve gradually over time. Rather than assuming persistence, the model lets it emerge from the data and, crucially, compares alternative ways in which agents might form expectations about exchange rate movements. The evidence reveals a consistent pattern. By the end of the sample period, currency substitution remains statistically and economically significant in all four countries. The dominant expectation mechanism is extrapolative: agents tend to look at recent depreciation and assume it will continue. This tendency creates a reinforcing loop—when currencies depreciate, expectations of further depreciation strengthen, and the incentive to hold foreign currency intensifies. What makes these findings particularly striking is that this dynamic does not vanish once inflation is stabilized. Even in periods of relative macroeconomic calm, substitution persists. Past instability leaves a lasting imprint on expectations, and concerns about the durability of policy reforms continue to shape monetary behavior. In several cases, ongoing depreciation against the U.S. dollar further validates these cautious beliefs. As a result, the findings suggest that currency substitution is not merely a mechanical residue of past inflation. It is sustained by the way people form and update expectations in environments marked by credibility challenges. Stabilizing inflation is therefore a necessary step, but it is not enough on its own. Durable confidence in the domestic currency requires rebuilding credibility in a way that gradually reshapes expectations and restores trust over time. Full article

The adoption of biogas technology in smallholder livestock systems is increasingly recognized as a dual solution for rural energy transition and livestock waste management; however, actual implementation remains limited due to low community readiness, particularly driven by knowledge gaps and resource constraints. This study examines the determinants of community readiness for biogas adoption in rural Indonesia, addressing the limited attention of prior studies to readiness factors at the household level. A cross-sectional survey of 98 smallholder cattle farmers was conducted using structured questionnaires, and the data were analyzed using descriptive statistics and multiple linear regression to identify key determinants of readiness. The results indicate generally positive perceptions toward biogas, with knowledge, prior waste processing experience, perception scores, education level, and herd size significantly influencing readiness (p < 0.05). While awareness of biogas benefits and willingness to process manure emerged as key drivers, limited technical knowledge and time and cost constraints remained major barriers, suggesting an awareness–adoption gap. These findings align with behavioral adoption frameworks, highlighting the roles of knowledge, perceived benefits, and enabling conditions in shaping adoption readiness. Policy interventions emphasizing capacity-building, financial incentives, and adaptable biogas technologies are therefore essential to support rural adoption. Full article

As the global imperative for climate neutrality intensifies, hydrogen (H₂) from fossil fuels remains central to decarbonizing hard-to-abate sectors. Conventional production via steam methane reforming (SMR), however, is carbon-intensive and, even with carbon capture and storage (CCS), incurs energy penalties and long-term storage constraints. This review develops a harmonized well-to-gate, market-oriented framework to evaluate methane pyrolysis (MP) relative to SMR and autothermal reforming (ATR), with or without CCS, moving beyond reactor-focused assessments toward system-level commercialization analysis. MP decomposes methane into hydrogen and solid carbon, avoiding direct CO₂ formation and the need for CCS infrastructure. Integrating with the reverse water–gas shift (RWGS) reaction enables flexible syngas production with adjustable H₂:CO ratios for methanol and chemical synthesis. A central finding is the dominant role of the “carbon lever”: MP generates approximately 3 kg of solid carbon per kg of H₂, making the carbon market’s absorptive capacity the primary scalability constraint. While carbon monetization can reduce levelized hydrogen costs, large-scale deployment would rapidly saturate existing carbon black and specialty carbon markets. Techno-economic evidence indicates that carbon prices above $500/ton are required to achieve parity with gray hydrogen, whereas $150–200/ton enables competitiveness with blue hydrogen. Lifecycle assessments further show that climate superiority over SMR or ATR with CCS requires upstream methane leakage below 0.5% and very low-carbon electricity. Commercial readiness varies, with plasma MP at TRL 8–9 and thermal, catalytic, and molten-media pathways remaining at the pilot or demonstration stage. Parametric decision-space analysis under harmonized boundary assumptions shows that MP is not a universal substitute for reforming but a conditional pathway competitive only under aligned conditions of low-leakage gas supply, low-carbon electricity, credible carbon monetization, and supportive policy incentives. The review concludes with a roadmap that highlights standardized carbon certification, end-of-life accounting, and long-duration operational data as priorities for commercialization. Full article

With the growing demand for trustworthy multi-party data sharing, federated learning has demonstrated broad potential in cross-entity collaborative modeling. However, it still faces challenges such as insufficient participant engagement, inaccurate contribution assessment, and the lack of dynamic profit-sharing mechanisms. Traditional incentive schemes, which typically rely on game-theoretic models or static rules, struggle to accommodate dynamic client participation and heterogeneous data distributions, thereby degrading the convergence efficiency and generalization performance of the global model. To address these issues, we propose a budget-aware closed-loop incentive allocation for federated learning with deep deterministic policy gradient (DDPG). The proposed approach constructs a DDPG-driven closed-loop framework in which the server manages system states, incentive decisions, and model aggregation, while clients autonomously adjust their data contribution levels. By formulating incentive allocation as a sequential decision-making problem, the mechanism jointly optimizes policy and value functions. A permutation method is introduced to ensure invariance to client ordering, and an Ornstein–Uhlenbeck process is employed to enhance exploration, thereby improving the adaptiveness and overall effectiveness of incentive allocation. Experimental results show that the proposed method significantly increases cumulative rewards and improves client data-sharing rates in high-dimensional dynamic environments. Compared with traditional fixed incentive schemes, the mechanism demonstrates clear advantages in adaptiveness, incentive effectiveness, and model performance. Full article

The built environment continues to encounter significant challenges related to waste generation and resource depletion, driving increased interest in circular economy strategies that extend material lifecycles and mitigate environmental impacts. This systematic review synthesises findings from 60 studies on waste-to-resource innovations across construction and household contexts. Although the existing literature predominantly addresses construction and demolition waste, this review foregrounds household operational waste, an area that remains insufficiently explored despite its importance for everyday resource recovery. The analysis examines how materials generated through routine use, maintenance, and minor renovation activities can be captured and redirected into productive resource streams, with particular attention to governance mechanisms such as Extended Producer Responsibility (EPR). The findings indicate that effective waste-to-resource systems depend on coherent regulatory frameworks and enforcement, economic incentives, enabling technologies, community engagement, and product design that facilitates reuse and disassembly. Key barriers include low public awareness, fragmented supply chains, high recovery costs, weak compliance mechanisms, and materials that are difficult to separate. The review concludes that improving waste-to-resource outcomes in the built environment requires coordinated action among producers, households, local authorities, and technology providers, and it articulates policy-relevant and community-oriented pathways to support more effective resource recovery systems. Full article

Evidence from observational studies plays a central role in shaping public policy in health, education, and financial regulation, where randomized experiments are rarely feasible. Propensity score matching (PSM) is a widely used method to approximate fair comparisons between treatment and control groups. Incorporating machine learning into the estimation of propensity scores can strengthen prediction and enhance the credibility of findings. However, stronger predictive models create a “predictability paradox”. As predictive accuracy improves, estimated propensity scores for treated and control units become more distinct when treatment assignment is strongly predictable from observed covariates, revealing limited overlap between groups. In the limit, near-perfect prediction produces near-complete separation between groups, rendering traditional matching infeasible and confining inference to a narrow subset of units near the boundary of the propensity score distribution, a setting analogous to a regression discontinuity design (RDD). Researchers thus face perverse incentives to use weaker models for statistically significant but spurious results. These dynamics jeopardize the reliability of evidence for policy. To safeguard decision-making, we propose a simple reform: require that studies using PSM disclose model error rates, including false positive and false negative rates, along with information on overlap and effective sample size. Full article

This work proposes an interval incentive and predictive interpolation-based proximal policy optimization (IP-PPO) scheme for automated guided vehicle (AGV) path planning, to achieve fast convergence, strong generalization and sufficient smoothness in the control actions. Firstly, a predictive interpolation method is integrated into the traditional proximal policy optimization (PPO) framework. Then, an enhanced vector field histogram is constructed to generate the safe interval state, which is incorporated into the observation space. Furthermore, a safe interval-based reward function is formulated to enhance the obstacle avoidance performance. The interval incentive mechanism, which includes the safe interval state and interval reward function, is integrated into the predictive interpolation-based PPO to construct the IP-PPO framework. Finally, comparative simulations demonstrate that the proposed IP-PPO scheme exhibits superior learning efficiency, generalization performance, and strong robustness against model uncertainties while maintaining high smoothness of AGV path planning. Full article