Search Results (3,177)

Despite two decades of participation in international carbon finance mechanisms and substantial forest carbon endowment, Ecuador lacks an integrated, cross-mechanism assessment of its carbon market trajectory. This study addresses that gap by applying an institutional economics framework to evaluate Ecuador’s experience under the Clean Development Mechanism (CDM), Reducing Emissions from Deforestation and Forest Degradation (REDD+), and the voluntary carbon market (VCM). Methodologically, the study applies a structured descriptive evidence synthesis drawing on four data corpora: UNFCCC/CDM registry records (IGES v13.7), official Ecuadorian legal and policy documents, program documentation for REDD+/GCF/LEAF/PECC, and peer-reviewed literature published between 2022 and 2025. Where figures diverged across sources, official registry values and disclosed payment records were prioritized. The principal findings are as follows: under the CDM (2006–2023), Ecuador registered 34 projects, of which only 14 (41%) issued Certified Emission Reductions (CERs) by 2020, accumulating $2.8 {\mathrm{MtCO}}_2$e—below the global CDM issuance rate of approximately 57% and below ex ante projections for the 34 registered projects (only 8%). Under REDD+, results-based payments totaling approximately USD 49.5 million have been disbursed through the Green Climate Fund and the REDD Early Movers program, with an additional USD 30 million committed under the LEAF Coalition at USD ${\mathrm{10/tCO}}_2$. Ecuador’s domestic voluntary market (PECC) is nascent, constrained by constitutional provisions limiting private appropriation of environmental services and by the 2024 presidential veto of proposed Organic Environmental Code reforms. The study concludes that Ecuador’s carbon market potential is real but contingent on legal certainty, transparent registries, conservative accounting, and credible benefit-sharing. This is the first integrated, integrity-centred cross-mechanism analysis for Ecuador, with implications for constitutional reform design and Article 6 readiness in forest-rich developing economies. Full article

Global land degradation metrics often rely on trend-based categories that miss cumulative severity, frequently misclassifying degraded areas as stable. To overcome this, we developed a Land Degradation Index (LDI) to assess degradation across Brazil on a 500 m grid for 2001 and 2021. The LDI integrates land-cover change legacy (deforestation age), ecosystem functioning (Gross Primary Productivity), and soil condition (Soil Organic Carbon) into a six-level gradient ranging from conserved to highly degraded. Results reveal that between 2001 and 2021, Brazil lost 50.5 million hectares of conserved land, while intermediate and severe degradation expanded by 53.5 million hectares. Conservation remained concentrated in the Amazon and Pantanal, whereas degradation intensified across the Atlantic Forest, Cerrado, and Caatinga, particularly along agricultural frontiers. Furthermore, while Indigenous Lands and Quilombola Territories act as vital conservation cores, the LDI reveals intensified degradation in their immediate surroundings, highlighting the intersection of biophysical decline, land conflicts, and socio-environmental vulnerability. The proposed index advances beyond conventional indicators, such as SDG 15.3.1, by incorporating both the intensity and variation of degradation processes into a unified analytical framework, providing a robust, reproducible framework to support Land Degradation Neutrality (LDN) targets, inform public policies, and guide inclusive territorial planning. Full article

The relationship between forest ecosystem services and human health has emerged as a key topic in forest economics and health policy research. This study develops a spatial modelling framework to quantify the health benefits of forest ecosystem services and proposes policy mechanisms to incorporate these benefits into governmental health strategies. Using county-level panel data from 66 administrative units in Zhejiang Province, China, covering the period 2013–2023, we analyse the relationship between forest-mediated air purification services and two population health outcomes: the incidence of respiratory diseases and cardiovascular disease mortality. We employ a Spatial Durbin Model (SDM) to estimate both direct and spatial spillover effects across county boundaries. The findings indicate that forest ecosystem services exert significant negative effects on adverse health outcomes, with spillover effects extending beyond administrative boundaries. The monetised health benefit of forests is estimated at approximately RMB 1108.6 per hectare per year, substantially exceeding current ecological compensation standards and suggesting systematic undervaluation of forest health services. Heterogeneity analysis reveals that health benefits are greater in urbanised regions and among vulnerable population groups, including the elderly. These findings provide an empirical basis for reforming health-oriented ecological compensation mechanisms and offer implications for sustainable land use governance aligned with SDG 3 (Good Health and Well-being) and SDG 15 (Life on Land). Full article

Mangrove ecosystems provide critical coastal protection, biodiversity support, and carbon storage, yet they remain vulnerable to degradation caused by coastal development, pollution, and climate-related pressures. This study monitors mangrove dynamics in Al-Birk, Asir Region, Saudi Arabia, using Google Earth Engine (GEE), multi-temporal Landsat imagery, spectral indices, and Random Forest (RF) classification. Landsat imagery from 2016 to 2021 was processed to derive NDVI, MSAVI2, EVI, and NDWI, and supervised RF classification was applied to map annual mangrove extent and associated land-cover classes. The RF classifier achieved an overall accuracy of 92.5% and a Kappa coefficient of 0.89. Results indicate that classified mangrove extent increased from approximately 1069 ha in 2016 to 1540 ha in 2021, representing a net gain of 471 ha and a 44% increase over the study period. A localized decline was detected between 2020 and 2021, indicating spatially uneven vegetation dynamics. The findings provide a spatial baseline for monitoring mangrove change and supporting coastal conservation planning in Saudi Arabia. While the detected expansion is temporally consistent with ongoing restoration initiatives, the study does not establish direct causality between policy interventions and observed spatial changes. Full article

Ensuring food security is a top priority for China, and non-grain production (NGP) of cropland can substantially reduce food production. As the core grain production base in Shaanxi Province and even Northwest China, the Guanzhong region’s evolution of NGP is very important. Based on the single-phase remote sensing data and the time-series curve, this study identifies explicit non-grain production (E-NGP) and implicit non-grain production (I-NGP) of cropland in the Guanzhong region from 2001 to 2020. Spatial analysis and gradient analysis are applied to characterize the spatiotemporal dynamics, differences in reversibility, grain loss, and driving factors of E-NGP and I-NGP. The results show that the area of cropland used for NGP in the Guanzhong region has gradually increased over the past two decades. In 2020, the area of E-NGP reached 4212.06 km², while that of I-NGP accounted for 8300.16 km². The total cumulative loss attributed to NGP in 2020 reached 11.58 million tons, and the grain loss caused by I-NGP was approximately twice that of E-NGP. Moreover, cropland used for I-NGP exhibits greater instability and reversibility, making it more susceptible to human intervention than that under E-NGP. The cropland used for E-NGP is mainly distributed around urban areas, where it is often converted into construction land. The cropland used for I-NGP gradually expands from north to south, with areas south of the Weihe River increasingly converted into economic fruit forests. E-NGP is driven by both terrain and socioeconomic factors, while I-NGP shows a stronger natural geographical dependence. This study defines the scale boundaries and driving factors of NGP in the Guanzhong region, reveals its substantial threat to grain production capacity, and provides theoretical support for regional policy implementation and the formulation of refined cropland protection policies in the Guanzhong region. Full article

The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers only a few parameters and a single objective. This research develops a simulation-based framework that integrates multiple EOQ-based inventory policies and performs multi-objective optimization using the NSGA-II algorithm. The framework optimizes total cost, fill rate, and average inventory level and finally generates a Pareto front as a result. To reduce computational costs, we use a machine learning-based random forest model, which replaces a significant amount of the simulations with predictions. This reduces the simulation cost to approximately one-sixth of the original, while the quality of the simulation changes only minimally, as the hypervolume value decreases by only 4%. The proposed framework can be used as an effective decision-support tool for inventory optimization under stochastic demand conditions. Full article

The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling. Full article

Forest tourism represents an important pathway for promoting green consumption, with forest parks serving as the primary platform for its development. The coordinated development of forest parks is therefore essential for achieving integrated economic, social, and ecological benefits. Investigating the coordination and coupling between ecological resilience and tourism economy in forest parks of the Yellow River Basin along with driving factors carried substantial practical significance for balancing regional economic development with ecological conservation. The present research developed an indicator system that was comprehensive and dynamic for assessing ecological resilience across forest parks in nine provinces of the Yellow River Basin during 2013–2023. To investigate patterns of spatiotemporal evolution and uncover underlying driving mechanisms, exploratory spatial data analysis was combined with a spatiotemporal geographically weighted regression model. The main findings are as follows: (1) The integrated levels of ecological resilience and tourism economy across the Yellow River Basin showed significant spatiotemporal heterogeneity. From north to south, a high–low–high spatial pattern was exhibited by ecological resilience, while a core concentration and gradient diffusion pattern was demonstrated by the tourism economy. (2) The coupling coordination level between forest park ecosystems and the tourism economy increased, with a growing number of provinces transitioning toward coordinated and near–dysregulated states, although pronounced regional disparities persisted. (3) Kernel density analysis indicated an overall improvement in coordination, accompanied by strong regional differentiation. The upper reaches displayed a unipolar leading pattern, while the middle and lower reaches showed multipolar differentiation and a pronounced “Matthew effect”. (4) Technological innovation emerged as the core driving factor, though its influence varied considerably across regions. Overall, these findings provide theoretical support and empirical evidence for policy formulation aimed at achieving a balance between ecological conservation and economic development in forest park systems. Full article

Nepal has undergone significant land-use and land-cover (LULC) changes from 2000 to 2020, driven by urbanization, agricultural shifts, and broader socioeconomic dynamics. This study analyzes historical changes and projects LULC dynamics for 2030, 2040, and 2050 across four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER). A CA–Markov modeling framework in TerrSet was used to simulate future land-use patterns, utilizing scenario-specific transition probability matrices and spatial constraints to reflect different socio-economic and policy assumptions. Under the BAU scenario, land-use change remains moderate, characterized by gradual urban expansion and limited forest decline. On the contrary, the RUD scenario predicts a drastic expansion of built-up areas by about 1.44 million ha, along with significant losses of cropland, bare soil, grassland, and forest, reflecting intensified development pressure. The FDTC scenario emphasizes agricultural expansion at the expense of forests, while urban growth remains limited. Conversely, the ALER scenario demonstrates strong ecological recovery driven by cropland abandonment and secondary vegetation regeneration, resulting in notable expansion of forest and other woody land. Overall, these four scenarios reveal sharply divergent land-use trajectories, ranging from rapid urban transformation to ecosystem restoration. These contrasting land-use pathways highlight the critical importance of integrated land-use policies that can proactively manage urban expansion, safeguard high-value agricultural and forest landscapes, and promote ecological restoration through incentives for agricultural land abandonment and secondary vegetation recovery, thereby ensuring long-term sustainability and climate resilience in Nepal. Full article

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article

To protect the endangered Northern Spotted Owl, the U.S. Fish and Wildlife Service established extensive conservation areas across the Pacific Northwest (PNW). While this policy effectively contributed to the preservation of an endangered species, it also generated significant short- and long-term impacts on the U.S. forestry market. This study investigates the impact of federal timber harvesting restrictions in the Pacific Northwest in the early 1990s on the U.S. softwood market, particularly on softwood planting in the South. By constructing and analyzing a panel dataset covering 537 counties in seven southern U.S. states from 1977 to 2007, the research finds that timber-harvesting restrictions triggered by the listing of the Northern Spotted Owl as threatened led to a significant increase in softwood planting rates in the Southern U.S. Previous studies have shown that set-asides can shift timber harvesting from one region to another and raise prices in the short term. This study illustrates a different outcome of set-asides: tree planting. We argue that accounting for long-term investment responses, such as tree planting, is critical when evaluating the impacts of forest policies, as these can significantly alter estimates of net carbon balance and overall market outcomes. Full article

New-energy plants and battery energy-storage stations increasingly depend on software-defined supervision, remote maintenance, and event-driven control, which makes cyber protection inseparable from operational responsiveness. This study presents a calibrated context-aware Security-as-a-Service orchestration framework, denoted SECaaS-CARO, for station-oriented adaptive risk control. The framework separates field assets, control services, security services, and an adaptive decision layer, and it uses a monotone nine-indicator risk score whose weights are calibrated from the training split rather than fixed heuristically. A validation-based threshold search maps that score to low-, medium-, and high-intensity service chains so that protection strength changes with session context instead of remaining static. A reproducible semi-synthetic dataset containing 17,000 station sessions was used to emulate operator login, remote maintenance, gateway misuse, and malicious command scenarios. Across 10 independently resampled 5000-session test streams, SECaaS-CARO achieved an F1 score of 0.973, a blocking success of 0.965, and the highest deployment utility of 1.173 while reducing mean latency to 21.28 ms compared with 27.06 ms for Logistic-Fixed and 28.15 ms for RandomForest-Fixed. The results indicate that an interpretable calibrated service-orchestration policy can preserve near-supervised detection quality while materially improving deployment-oriented efficiency for new-energy and energy-storage stations. Full article

The National Register of Historic Places (NR) is a structured artifact of meaning-making that encodes disciplinary values linking architectural and cultural significance to wealth and stylistic distinction. In doing so, it systematically underrepresents vernacular, working-class, and the built environments of racially and ethnically marginalized communities. This paper uses artificial intelligence (AI) to examine how that meaning is constructed. We analyze the preservation record across three scales: a national dataset of 100,117 NR listings (1966–2025), a state-level profile of Illinois’s 1997 NR listings, and a close analysis of Lake Forest, Illinois, a community whose exceptional concentration of NR-listed estate architecture makes it an ideal site for examining how preservation significance has been defined and what it excludes. Two parallel AI methods are applied to eighteen Lake Forest nomination documents and their associated photographs. Natural Language Processing (NLP) analyzes nomination text to trace how preservation professionals connect buildings to cultural value; blind AI image analysis examines the same properties to assess how a model trained on cultural imagery constructs visual meaning independently. NLP analysis reveals a corpus dominated by architectural description, with social history, landscape, and labor systematically underrepresented. The visual analysis confirms and amplifies the nomination record’s class-based assumptions while reproducing the same omissions regarding labor, diversity, and community context. These findings inform debates about AI’s potential to audit existing listings and support nominations for underrepresented property types, while showing that without deliberate corrective design and policy reform, such tools are as likely to replicate the preservation system’s inequities as to repair them. Full article

Organic farming has gained increasing relevance worldwide due to its environmental benefits and its prominent role in sustainable food systems; however, the persistence of organic farms remains uneven across regions, particularly within the European Union. While the number of organic farms has grown overall in the EU, significant exits from organic production highlight the need to better understand the factors shaping farm survival, especially in newer Member States, where organic conversion and maintenance support schemes are often implemented through area-based CAP payments. This study aims to identify the structural and contextual determinants of short-term organic farm persistence in Hungary within a broader European context. Using farm-level data for the period 2020–2023, including Standard Output (SO) indicators, we applied a combined modelling framework based on Logistic Regression, Decision Trees, and Random Forest algorithms to assess the relative importance of economic, structural, and regional variables. The results show that organic farm persistence is primarily driven by structural characteristics such as farm size, economic scale, degree of conversion to organic farming and regional embeddedness, while production specialization and organizational features play a secondary, conditional role. The convergence of results across modelling approaches indicates that survival is shaped by hierarchical structural constraints rather than isolated management decisions. Our findings suggest that policy measures aiming to stabilize and expand the organic sector should move beyond uniform incentives, such as area-based payments, and should place greater emphasis on the structural conditions of farms and region-specific support mechanisms. Full article

The Earth’s ecosystems are rapidly deteriorating due to human activities. Habitats are being lost or degraded, and associated wildlife species are declining or becoming extinct at unprecedented rates. The study area, the prefectures of Rodopi and Evros, is a Greek biodiversity hotspot containing degraded habitats, such as forests and wetlands, that are critical for many threatened wildlife species. This situation calls for conserving threatened wildlife habitats, which requires considerable funds. A structured questionnaire was used to evaluate willingness to pay (WTP) for wildlife habitat conservation. We conducted personal interviews with residents of the study area, using a sample of 849 citizens from the two regions determined through stratified random sampling design, with equal allocation to the strata. The mean annual WTP per household was estimated at EUR 21.3, yielding a total of EUR 790,000 from households in the study area. Pro-environmental behavior was positively associated with WTP. Females and those with higher household income reported higher WTP than males and those with lower household income. Government agencies were preferred over hunting clubs and environmental NGOs for implementing programs to conserve local wildlife habitats. Findings will be most useful if incorporated into policies to (a) secure the funds necessary to implement wildlife habitat conservation programs in the area and (b) increase transparency and trust between conservation entities and the local community. Full article