Search Results (705)

Background/Objective: Achievement of Sustainable Development Goals SDG 2 (child nutrition) depends upon SDG 6 (water insecurity) and SDG 13 (climate action) in multiple ways. However, the current climate–nutrition literature mostly considers water’s effects on nutrition through agriculture and food production. Here, we identify the climate’s impact on child nutrition through its effect on both household food and water security and on their interaction across varying environmental contexts. Methods: Using nationally representative data from India, we estimate the climate’s direct association with household water access (time spent fetching water), and both direct and indirect association with household food security (women’s dietary diversity), and child’s dietary diversity and nutrition (HAZ score). Data from 42,567 women and 39,667 children (6–23 months) are analyzed using linear regression and structural equation modeling. Results: A unit increase in rainfall is linked to an 18 percent decrease in time to water and an 8.3 percent increase in women’s dietary diversity score. A temperature increase is associated with an increase in time to water and decreased women’s dietary diversity. Time to water mediates the association of temperature and rainfall with women’s dietary diversity, child’s dietary diversity and child’s HAZ score. Households in regions of higher water availability are associated with increased dietary diversity, increased HAZ, and decreased time to water; however, the interaction between climate and regional water availability shows varying effects. Conclusions: Climate is associated with household food and water security, which together mediate its association with nutrition. These findings call for broadening the climate action framework to explicitly recognize the multidimensional linkages between SDG 6 and SDG 2. Full article

Standard cryptocurrency transaction cost models assume flat geometry and assign execution cost as a proportional fee. This paper proposes GEODEX, a framework that models execution slippage as the geodesic arc length on the Fisher information manifold of a Markov-switching GARCH maximum-entropy model, augmented by a joint curvature–topological fragmentation alarm. The Curvature-Fragmentation Law (Proposition 2) is an analytically derived heuristic. Its empirical validity is confirmed across four crisis episodes. Ablation confirms that each geometric component contributes uniquely: removing the geodesic increases mean squared prediction error by 2.9%, removing topological data analysis by 2.1%, and removing curvature by 1.5%. On five cryptocurrency markets (BTC, ETH, XRP, LTC, and BCH), over 2253 daily observations, the framework achieves competitive prediction error and is the only single-signal model retained in the Model Confidence Set at $\alpha =0.10$ against eight benchmarks. A joint curvature–topological alarm fires a median of two days before price-based circuit breaker thresholds across four crisis episodes, including the Terra collapse (May 2022) and FTX bankruptcy (November 2022). Online inference requires under one second; full offline calibration requires approximately 28 h. The framework requires no additional data beyond the upstream estimation pipeline and supports SDG 10 (Reduced Inequalities) and SDG 16 (Strong Institutions) by enabling accessible geometric liquidity intelligence for regulators and smaller market participants. Full article

Extended producer responsibility (EPR) is intended to link producer design decisions to end-of-life costs, but collective EPR schemes typically weaken this link by routing funding through producer responsibility organisations. We develop a multi-level framework of consumer-integrated environmental protection (CIEP) and argue that individual producer responsibility (IPR), where producers bear product-specific end-of-life liability, can function as a governance mechanism that reconnects design, consumer behaviour and waste governance. This paper is a qualitative multiple-case research study—not a systematic review—which draws on three funded research projects: (i) small and medium-sized enterprise (SME) tools for design-for-recyclability, (ii) an artificial intelligence (AI) application for household waste sorting, and (iii) closed-loop recycling of fishing gear in Vietnam. Within the first project (ToCoReRaM), a PRISMA-based systematic review of web-accessible circular economy tools finds that only 2 of 23 tools are SME-accessible through standard web searches. The AI-based waste-sorting application achieves approximately 75% classification accuracy under real-world conditions. The fishing gear study demonstrates technical and economic viability of closed-loop recycling, and a survey of more than 1500 Vietnamese fishers finds 95.8% willingness to return used gear given appropriate incentives. Together, the cases show that effective circular governance requires four complementary elements: IPR-based producer accountability, SME-accessible design tools, digital consumer guidance at the point of disposal, and context-sensitive governance capacity. These findings inform policy pathways for Sustainable Development Goal (SDG) 12 and SDG 14. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

Agroforestry systems enhance soil health and sustainability in tropical agriculture, yet nutrient dynamics in cacao-based systems remain underexplored. This study evaluated litter yield, decomposition rates, and nutrient release in three cacao (Theobroma cacao L.) agroforestry arrangements in Tumaco, Colombia, using a randomized block design over 150 days. Litter yield was highest in the Cedrela odorata and T. cacao system (12,040.08 kg/ha) and lowest in the Carapa guianensis and T. cacao (3279.84 kg/ha). Decomposition rates were highest for C. guianensis (67.5%), followed by T. cacao (64%) and C. odorata (59.8%). Nutrient release followed the sequence Ca > N > Mg > K > P. Agroforestry systems improve soil fertility but require integrated nutrient management to meet cacao’s needs, contributing to sustainable agriculture aligned with SDG 2 and SDG 15. Full article

This paper examines how artificial intelligence can support sustainability assessment in cultural industries, using national film industries as a test case. The Film Industry Sustainability Index (FISI) is introduced as a composite indicator covering cultural diversity, economic resilience, and Sustainable Development Goal (SDG) alignment for 42 national economies from 2005 to 2023. Knowledge-Enhanced Mamba (KE-Mamba), a selective state-space forecasting model, is then proposed to combine annual panel indicators with country-level film-industry knowledge graph (KG) embeddings and large language model (LLM)-derived screenplay-oriented narrative proxies from film synopses. To reduce factual errors in title-level narrative scoring, the LLM is anchored to verified United Nations Educational, Scientific and Cultural Organization (UNESCO) records and the European Audiovisual Observatory’s LUMIERE film-admissions database using rank-one model editing (ROME). On the 2020–2023 held-out test period, KE-Mamba achieves a composite FISI mean absolute error (MAE) of 0.0389, a mean absolute percentage error (MAPE) of 5.61%, and an $R^2$ of 0.934, outperforming autoregressive integrated moving average (ARIMA), tree-based, long short-term memory (LSTM), and base Mamba baselines. Additional robustness checks using a pre-pandemic split, two-way fixed-effects panel regression, alternative FISI weighting schemes, KG embedding ablations, and human validation of LLM narrative scores support the reliability of the proposed framework. Policy simulations are interpreted as model-based projected associations rather than causal estimates. The results show that knowledge-enhanced sequence models can provide transparent forecasting support for sustainable cultural-industry policy. Full article

Environmental degradation caused by unsustainable farming practices has depleted soil resources across sub-Saharan Africa. Conservation agriculture (CA) has been promoted to reverse this damage, yet outcomes remain variable, and the role of long-term training is underexplored. Using propensity score matching with 238 smallholder households across five communities in Ghana, we examine the impacts of long-term CA training. Specifically, we assess whether participation in a training program characterized by repeated engagement and follow-up workshops improves yields, farmer knowledge of soil health, and soil indicators (nitrogen and carbon). Farmers receiving long-term CA training did not exhibit significantly better soil chemical metrics. However, they demonstrated significantly more accurate knowledge of soil health (nitrogen, p < 0.001; carbon, p < 0.05), produced a 10.7% higher maize yield (kg/acre) (p < 0.001), and reported fewer soil problems, including fertilizer runoff, top-soil erosion, and waterlogging, compared to conventional farmers (all p < 0.05). We conclude that long-term CA training enhances farmer knowledge and maize yields, suggesting it is a critical intervention for improving productivity and farm management resilience, even where direct improvements in measured soil metrics are not immediately detectable. These findings highlight the need for training programs to emphasize the full suite of CA principles and for evaluation timeframes of 5–10 years to capture soil regeneration. Full article

The development of sustainable and efficient plant growth substrates is crucial for modern agriculture. This study assessed the potential of plant pellets formulated from various lignocellulosic residues, either with or without bamboo biochar (BB-char) and arbuscular mycorrhizal fungi (AMF), to support seed germination and early seedling growth. Four types of residues, including coconut coir (CO), corn cob (CC), leaves from the genus Dipterocarpus (DL), and teak leaves (TL), were combined with soil and paper waste to produce eight pellet formulations, with commercial peat pellets serving as a control. Chemical analyses revealed significant variation among the pellet types, with pH values ranging from 6.40 to 7.65, electrical conductivity (EC) from 3.64 to 11.62 mS cm⁻¹, and differences in organic matter, carbon, and nutrient contents [nitrogen (N), phosphorus (P), potassium (K)], reflecting the influence of residue type and the addition of BB-char and AMF. Phytotoxicity screening using aqueous extracts demonstrated species-specific responses, with cucumber exhibiting high tolerance across treatments, whereas chili seeds were more sensitive. Final germination percentage (FGP) and seedling growth assays in greenhouse conditions showed that pellets derived from CC and CO, particularly when combined with BB-char and AMF (T6 and T7), enhanced shoot and root development in carrot, chili, cucumber, and tomato, approaching the performance of commercial peat pellets. In contrast, DL- and TL-based pellets resulted in lower germination and growth. These findings indicate that both the physicochemical properties of lignocellulosic wastes and the combination of BB-char and AMF are important factors influencing pellet efficacy, highlighting the potential of CC- and CO-based pellets as sustainable peat alternatives for early-stage plant cultivation. Full article

The European Union (EU) and national governments have set clear targets to reduce agricultural emissions, including ammonia from manure spreading practice, with regulations such as the Ambient Air Quality (AQ) and Clean Air Directives, the Common Agricultural Policy (CAP), and the Green Deal, with implication for ecosystem services and landscape planning, reflecting broader environmental sustainability objectives including those addressed by the Sustainable Development Goals (SDGs). Informative Inventory Reports (IIRs) are critical tools within the EMEP/EEA framework for monitoring long-range transboundary air pollution. They utilize three distinct methodological tiers (Tiers 1, 2, and 3) to estimate emission data across Europe. Despite the availability of Earth Observation (EO) data and products from the Copernicus Programme current estimation methods still rarely integrate EO information to produce spatially explicit estimates. This paper reviews current methodologies for estimating ammonia in IIRs and in scientific literature, including advanced methods not yet implemented in official inventories but potentially capable of supporting more spatially explicit and process-oriented estimation. A Medium Effort Methodology (MEM) is identified among those reviewed as a representative methodological pathway for integrating EO information with Tier 3 approaches. Building on this, the paper explores the association between specific EO data and Copernicus products, and input variables required by MEM, identifying opportunities and barriers for environmental monitoring with potential relevance to sustainable agriculture. Full article

Amid growing global attention to corporate sustainability and responsible investment, the disclosure of Sustainable Development Goals (SDGs) has emerged as an important component of non-financial reporting. However, the extent to which SDG disclosure contributes to firm value remains underexplored, particularly in emerging markets. This study examines the association between SDG disclosure in corporate reports and firm value among 660 publicly listed companies across four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. SDG disclosure is measured using 17 SDG indicators derived from the Refinitiv database and should be interpreted as a measure of disclosure breadth rather than disclosure quality or depth. The analysis begins with descriptive statistics to illustrate the distribution of key variables, followed by ANOVA to assess differences in SDG disclosure across countries and industries. Hypothesis testing is then conducted using multiple regression analysis with robust standard errors, with firm value proxied by price-to-book value (PBV). Several robustness checks are performed, including winsorised regression, year-by-year regressions, and regression models incorporating country and industry dummy variables. The results indicate that SDG disclosure is positively associated with firm value, although the relationship is interpreted as correlational rather than causal because of the short observation period and potential endogeneity. The findings also show that SDG disclosure is unevenly distributed across goals and countries, with SDG 8 and SDG 13 receiving the highest attention, while SDG 2 and SDG 14 remain among the least disclosed. These results highlight the importance of sustainability transparency in shaping market valuation and underscore the need for more balanced, comparable, and quality-oriented sustainability reporting frameworks across the region. Full article

Although rural livelihoods act as a critical mediator between poverty alleviation policies and sustainable outcomes, the spatial heterogeneity of this interaction remains underexplored within those agrarian systems that are crucial for food production. This study examines how China’s Targeted Poverty Alleviation policies shape livelihood strategies and the livelihood diversity of rural households across different spatial contexts in Jilin Province, a main grain production region of Northeast China. Using survey data from 2306 households, this study employs multiple logistic and linear regression models. The results indicate that (1) industrial and employment policies are associated with development-oriented strategies, whereas enterprise-driven and cash transfer policies tend to reinforce asset-based or welfare-dependent livelihoods; (2) these policy effects exhibit significant spatial heterogeneity, mediated by local agricultural productivity conditions, labor endowments, and off-farm livelihood availability; and (3) industrial policies show stronger associations with agricultural livelihoods in the east, while financial policies are more effective in sustaining agricultural engagement in the capital-constrained west. Integrating the Sustainable Livelihoods Framework with a spatial lens, this study shifts the focus of policy assessment from static outcome metrics to process-oriented analysis and reveals the mechanisms underlying the spatial divergence of livelihood strategies, providing a nuanced analytical framework for assessing the impacts of PAPs across diverse agricultural contexts. Based on these findings, this study highlights that spatially differentiated, livelihood context-sensitive policies are essential for securing sustainable and long-term poverty reduction in grain production regions, offering a replicable template for policy evaluation and practical implications for achieving SDGs 1 and 2 in agrarian regions. Full article

Greenhouse vegetable cultivation in tropical regions is often affected by high temperature, unstable humidity, and irrigation management problems. This study presents a pilot-scale case study of Green Oak lettuce cultivation using an IoT-based sensor monitoring and automated irrigation control system in Phra Nakhon Si Ayutthaya Province, Thailand. The system used AM2315C, BH1750, NPK, and flow sensors connected to ESP32. Data were transmitted to the ThingsBoard platform for real-time environmental monitoring and irrigation control. The greenhouse temperature averaged 33.21 ± 3.61 °C, while relative humidity averaged 71.55 ± 9.66%. The average daytime light intensity was 16,976 ± 409 lux. Nitrogen (N), phosphorus (P), and potassium (K) concentrations remained within ranges of 62.42–74.57, 76.46–84.30, and 71.46–79.30 mg/kg, respectively. Economic evaluation demonstrated favorable feasibility, with a water use efficiency (WUE) of 0.63 kg/L, return on investment (ROI) of 40%, benefit–cost ratio (BCR) of 1.6, and payback period of approximately 2.5 years. The developed system demonstrates potential for supporting sustainable greenhouse agriculture and contributes to SDG 2, SDG 6, SDG 12, and SDG 13 under tropical agricultural conditions. Full article

Sustainable agricultural development in rural sub-Saharan Africa increasingly depends on coordinated governance of energy and water resources. Despite the growing deployment of solar photovoltaic water pumping systems (SPVWPS), little is known about how the institutional framework shapes SPVWPS effectiveness for productive agricultural use in rural Tanzania. Drawing on systems thinking concepts, specifically hierarchy, interaction, and interconnectedness, this study analyses the institutional frameworks governing energy and water provision for irrigation and livestock keeping across three rural Tanzanian communities. A mixed-methods design was employed, with qualitative inquiry as the primary mode; 65 household surveys, nine semi-structured interviews with community leaders, SPV developers, and local officials, and seven focus group discussions with farmers and livestock keepers were conducted across the three study areas. National energy and water policy documents, reports, and strategic plans were also reviewed to contextualise the institutional frameworks governing energy and water delivery in rural areas. Findings reveal limited coordination among stakeholders, particularly between NGOs, government agencies (REA, RUWASA, and NIRC), and local communities in the planning and implementation of SPVWP projects. Top-down delivery mechanisms marginalised community feedback, undermining local ownership and limiting the productive use potential of installed systems. This study proposes an integrated institutional framework that combines systems thinking with bottom-up and top-down approaches, explicitly embedding structured feedback mechanisms and aligning stakeholder roles across all governance levels. The framework was validated through interviews with experts in the rural energy and governance field, confirming its practical relevance and applicability to rural energy–water governance. The framework offers actionable guidance for policymakers and development practitioners seeking to strengthen institutional coordination in rural energy–water–agriculture governance, contributing to progress towards SDG 7 and SDG 2 across sub-Saharan Africa. Full article

Vehicle replacement programs have become an important policy instrument for promoting sustainable mobility and accelerating the transition from gasoline vehicles (GVs) to new-energy vehicles (NEVs). In such markets, government subsidies, manufacturer competition, and trade-in strategies jointly shape not only manufacturers’ decisions but also environmental and social outcomes. Motivated by this issue, this study develops a game-theoretic model to examine competitive manufacturers’ optimal pricing and trade-in strategies as well as the government’s socially optimal subsidy level. Furthermore, using numerical examples, we reveal the effect of the action sequences of NEV and GV manufacturers on manufacturers’ pricing and trade-in decisions as well as the government’s socially optimal strategy. The results indicate that (1) manufacturers’ pricing and trade-in strategies are not only affected by government subsidies but also closely related to the gap between consumers’ mileage anxiety and degree of alternative preference. (2) The environmental benefits of recycling old products affect government subsidies on the consumption and production side at different levels. (3) NEV and GV manufacturers diverge in terms of how they respond to the sequence of their business strategies. This study contributes to the sustainability literature by showing how subsidy design and manufacturers’ decision sequence jointly affect sustainable consumption, recycling-related environmental benefits, and the transition toward cleaner mobility. The findings provide implications for sustainable vehicle replacement and support Sustainable Development Goal (SDG) 11, SDG 12, and SDG 13 by promoting cleaner mobility, responsible consumption, and climate action. Full article

This study explores the interaction mechanisms between population and environment systems within the context of high-quality development (HQD), providing empirical insights for developing countries navigating rapid urbanization. The existing literature often focuses on regional macro-averages, which may obscure internal spatial structural heterogeneity and the phenomenon of bottleneck shifts within urban agglomerations (UAs). Focusing on six typical UAs in China from 2011 to 2023, we constructed a multi-dimensional evaluation system and utilized an optimal parameters-based geographical detector (OPGD) and an obstacle degree model (ODM) to decode the spatiotemporal evolution of these systems. The results demonstrate that: (1) Both population and environment subsystems have improved steadily. Ecological carrying capacity has increased significantly, and the primary systemic constraint has transitioned from the “environmental bottom line” to the “population dividend,” with several super/mega cities converging toward a synchronous development interval. (2) The modified coupling coordination degree (MCCD) exhibits an overall upward trend. While eastern UAs demonstrate core-driven synergistic evolution, central and western UAs face risks of a “single-core siphon effect” and “peripheral hollowing-out,” leading to pronounced spatial polarization. (3) The OPGD analysis reveals that the driving efficiency of large-scale traditional infrastructure investment has experienced a marginal decline, whereas economic fundamentals and technological innovation have emerged as core drivers for non-linear enhancement. (4) The ODM confirms that traditional environmental pressures have been substantially alleviated. The core constraints have transitioned to the population and economic dimensions, with labor productivity and science and technology (S&T) expenditure identified as the primary obstacles. Aligning with the United Nations Sustainable Development Goals (SDGs), our findings may suggest that policy focus should shift from physical spatial expansion toward “soft connectivity” based on institutional and technological spillovers. We recommend establishing cross-regional coordination mechanisms to mitigate the siphon effects of core cities and transitioning policy priorities from ecological defense to high-quality population development. Full article