Search Results (5)

This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha⁻¹, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha⁻¹, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha⁻¹). Modeled SOC changes ($\mathsf{\Delta }\mathrm{SOC}$) were modest, with small gains (2.06 t C ha⁻¹) and localized losses (−9.96 t C ha⁻¹), producing a net mean increase of only 1.44 t C ha⁻¹. These values are substantially lower than field-based estimates (5–15 t C ha⁻¹), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article

University campuses in rapidly urbanizing regions face increasing pressure to balance infrastructure development with environmental sustainability, yet their carbon storage potential remains largely unexplored in sub-Saharan Africa. This study assessed land use changes, carbon storage dynamics, and economic valuation across three Ghanaian universities, University of Ghana (UG), Kwame Nkrumah University of Science and Technology (KNUST), and University of Cape Coast (UCC), from 2017 to 2023, and evaluated five future scenarios using the InVEST carbon model. Land use analysis employed ESRI 10 m annual land cover data, while carbon storage was estimated using regionally appropriate carbon pool values, and economic valuation applied Ghana’s social cost of carbon ($0.970/tCO₂). Historical analysis revealed substantial carbon losses: UG declined by 17.1% (19,695 Mg C), KNUST by 29.5% (20,063 Mg C), and UCC by 7.9% (3292 Mg C), due to tree cover conversion to built areas. Scenario modeling demonstrated that infrastructure-focused development would cause additional losses of 4211–6891 Mg C, while extensive tree expansion could increase storage by 1686–5227 Mg C. Economic analysis showed tree expansion generating positive net present values ($1612–$5070), while infrastructure development imposed costs (−$4028 to −$6684). These findings provide quantitative evidence for sustainable campus planning prioritizing carbon conservation in tropical institutional landscapes. Full article

This study evaluates the economic feasibility of offshore wind farm development on idle coastal areas in Korea, focusing on the Wando Geumil Offshore Wind Farm (GOWF) as a representative case. Offshore wind farms are increasingly recognized as key contributors to achieving carbon neutrality, and Korea’s coastal idle zones offer strategic potential for large-scale deployment with minimal land-use conflict. To address market uncertainty—particularly the sensitivity of revenues to the Renewable Portfolio Standard (RPS) and Renewable Energy Certificate (REC) weight—this research applies both the Discounted Cash Flow (DCF) method and Real Options Analysis (ROA), incorporating expansion and contraction scenarios. Using eleven years of historical System Marginal Price (SMP) and REC data, we estimate price volatility via a Geometric Brownian Motion (GBM) model (σ = 23.04%). The DCF results indicate a negative Net Present Value (NPV) of −313.7 million USD, suggesting baseline infeasibility. In contrast, ROA adds strategic value, with the expansion option yielding 69.6 million USD and the contraction option 2.1 million USD in additional project value. These findings demonstrate that integrating policy-driven revenue uncertainty into ROA substantially alters investment recommendations, offering practical guidance for optimizing offshore wind farm deployment on Korea’s idle coastal sites. Full article

The cattle sector plays a critical role in Peru’s agricultural economy, yet it faces challenges related to low productivity and environmental degradation. Sustainable alternatives like silvo-pastoral systems (SPSs) offer promising solutions to enhance both economic returns and ecological outcomes in cattle farming. This study examines the economic viability of an intensive SPS (SPSi) compared to traditional monoculture grass systems in San Martín, Peru. The SPSi under study is in the evaluation phase, integrates grasses, legumes, shrubs, and trees, and has the potential to enhance cattle farming profitability while simultaneously offering environmental benefits such as improved soil health and reduced greenhouse gas emissions. Through a discounted cash flow model over an eight-year period, key profitability indicators—Net Present Value (NPV), Internal Rate of Return (IRR), Benefit–Cost Ratio (BC), and payback period—were estimated for four dual-purpose cattle production scenarios: a traditional system and three SPSi scenarios (pessimistic, moderate, and optimistic). Monte Carlo simulations were conducted to assess risk, ensuring robust results. The results show that the NPV for the traditional system was a modest USD 61, while SPSi scenarios ranged from USD 9564 to USD 20,465. The IRR improved from 8.17% in the traditional system to between 26.63% and 30.33% in SPSi scenarios, with a shorter payback period of 4.5 to 5.8 years, compared to 7.98 years in the traditional system. Additionally, the SPSi demonstrated a 30% increase in milk production and a 50% to 250% rise in stocking rates per hectare. The study recommends, subject to pending validations through field trials, promoting SPSi adoption through improved access to credit, technical assistance, and policy frameworks that compensate farmers for ecosystem services. Policymakers should also implement monitoring mechanisms to mitigate unintended consequences, such as deforestation, ensuring that SPSi expansion aligns with sustainable land management practices. Overall, the SPSi presents a viable solution for achieving economic resilience and environmental sustainability in Peru’s cattle sector. Full article

In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (B_u1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (B_u2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation. Full article