Search Results (14)

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

Against the backdrop of increasing extreme weather events associated with global climate change, regulating carbon dioxide emissions, a primary contributor to atmospheric warming, has emerged as a pressing global challenge. Focusing on China as a representative case study of major developing economies, this research examines industrial carbon emission patterns during 2001–2022. Methodologically, it introduces an innovative analytical framework that integrates the Generalized Divisia Index Method (GDIM) with the Low Emissions Analysis Platform (LEAP) to both decompose industrial emission drivers and project future trajectories through 2040. Key findings reveal that:the following: (1) Carbon intensity in China’s industrial sector has been substantially decreasing under green technological advancements and policy interventions. (2) Industrial restructuring demonstrates constraining effects on carbon output, while productivity gains show untapped potential for emission abatement. Notably, the dual mechanisms of enhanced energy efficiency and cleaner energy transitions emerge as pivotal mitigation levers. (3) Scenario analyses indicate that coordinated policies addressing energy mix optimization, efficiency gains, and economic restructuring could facilitate achieving industrial carbon peaking before 2030. These results offer substantive insights for designing phased decarbonization roadmaps, while contributing empirical evidence to international climate policy discourse. The integrated methodology also presents a transferable analytical paradigm for emission studies in other industrializing economies. Full article

The need for integrated governance of water–energy–land–food (WELF) systems has become paramount in achieving sustainable low-carbon transitions, yet policy consistency across these interdependent sectors remains critically underexplored. This study presents the first systematic assessment of policy consistency and synergy within China’s WELF framework, employing an innovative mixed-methods approach that combines a modified Policy Modeling Consistency (PMC) Index with Content Analysis Methodology (CAM). Policy consistency follows a clear hierarchy: energy (PMC = 9.06, ‘Perfect’), water (8.26, ‘Good’), land (7.03, ‘Acceptable’), and food systems (6.91, ‘Acceptable’), with land–food policies exhibiting critical gaps in multifunctional design. Policy synergy metrics further reveal pronounced sectoral disparities: energy (PS = 0.89) and water (0.81) policies demonstrate strong alignment with central government objectives, whereas land (0.68) and food (0.64) systems exhibit constrained integration capacities due to uncoordinated policy architectures and competing sectoral priorities. Building on these findings, we propose three key interventions: (1) institutional restructuring through the establishment of an inter-ministerial coordination body with binding authority to align WELF sector priorities and enforce consistent and synergy targets, (2) the strategic rebalancing of policy instruments by reallocating fiscal incentives toward nexus-optimizing projects while developing innovative market-based mechanisms for cross-sectoral resource exchange, and (3) adaptive governance implementation through regional policy pilots, dynamic feedback systems, and capacity-building networks to enable context-sensitive WELF transitions while maintaining strategic consistency and synergy. These recommendations directly address the structural deficiencies in WELF governance fragmentation and incentive misalignment identified through our rigorous analysis, while simultaneously advancing theoretical discourse and offering implementable policy solutions for achieving integrated low-carbon transition. Full article

This study proposes a three-level meta-frontier framework enhanced with machine learning-driven projection methods to address the dual heterogeneity in carbon emission efficiency analysis arising from regional disparities and industrial diversification. Methodologically, we introduce two novel projection combinations—“exogenous-exogenous-accumulation (E-E-A) and exogenous-exogenous-consistent (E-E-C)”—to resolve the inconsistency of technology gap ratios (TGRs > 1) in traditional nonradial directional distance function (DDF) models. Reinforcement learning (RL) optimizes dynamic direction vectors, whereas graph neural networks (GNNs) encode spatial interdependencies to constrain the TGR within [0, 1]. Empirical analysis of 60 countries reveals that (1) E-E-C eliminates the TGR overestimation by 12–18% in energy-intensive sectors (e.g., reducing Asia’s secondary industry $TG{R}_1$ from 1.160 to 1.000); (2) industrial heterogeneity dominates inefficiency in Asia (IHI = 0.207), whereas management gaps drive global secondary sector inefficiency (MI = 0.678); and (3) policy simulations advocate for decentralized renewables in Africa, fiscal incentives for Asian coal retrofits, and expanded EU carbon border taxes. Computational enhancements via Apache Spark achieve a 58% runtime reduction. The framework advances environmental efficiency analysis by integrating machine learning with meta-frontier theory, offering both methodological rigor (via regularization and GNN constraints) and actionable decarbonization pathways. Limitations include static heterogeneity assumptions and data granularity gaps, prompting the future integration of IoT-enabled dynamic models. Full article

This study addresses the resource-constrained project scheduling problem with flexible structures and uncertain activity durations. The problem is formulated as a Markov decision process, with the optimal policy determined through stochastic dynamic programming. To mitigate the curse of dimensionality in large-scale problems, several approximate methods are proposed to derive suboptimal policies. In addition to traditional methods based on priority rules and metaheuristic algorithms, we focus on the application of rollout algorithms. To improve the computational efficiency of the rollout algorithms, only the best-performing priority rules are employed for action evaluation, and the common random numbers technique is also incorporated. Experimental results demonstrate that rollout algorithms significantly outperform priority rules and metaheuristics. The common random numbers technique not only enhances computational efficiency but also improves the accuracy of action selection. The post-rollout algorithm reduces computation time by 44.37% compared to the one-step rollout, with only a 0.02% performance gap. In addition, rollout algorithms perform more stably than other methods under different problem characteristics. Full article

This study introduces an evolutionary game model to investigate the strategic interaction among government regulatory agencies, shopping center investors, and tenants in the global energy-saving renovation market. The focus is on three innovative aspects. Firstly, the model reveals that positive tenant behavior can stimulate investors’ participation in energy-saving renovation projects by triggering potential market demand, thereby establishing a dynamic balance between supply and demand. This viewpoint has been previously overlooked in energy renovation research. Secondly, the model demonstrates the dynamic transformation of government regulatory strategies. In the early stages of market development, direct intervention through subsidies and penalties is crucial, and investors’ decisions are constrained by both returns and costs. When returns exceed the cost premium, investors actively participate, and policy incentives lower early cost barriers to promote market expansion. As the market matures, a transition toward policy guidance optimizes sustainable outcomes. Thirdly, extensive numerical simulations have confirmed the existence of multiple stable equilibrium states under different incentive and cost conditions, providing new evidence for the stability and adaptability of the energy-saving renovation market. These findings significantly advance the theoretical understanding of multi-stakeholder interactions in green building transformation and provide practical guidance for developing adaptive and effective policy frameworks. Full article

This study focuses on the land transfer intentions of migrants and surrounding villagers in the SZ resettlement area of BS City, Guangxi. It systematically analyzes the coupling coordination relationship between migrants’ land transfer-in intentions and the land transfer-out intentions of surrounding villagers, verifying the practical value of the “Shared Land Resource Model” in the resettlement area and its surroundings. The study yields the following key conclusions: (1) there is a strong coupling between the land demand intentions of migrants and the land supply intentions of surrounding villagers, yet the actual coordination in the transfer process is limited, which constrains resource allocation efficiency and prevents land transfer from fully utilizing shared resources; (2) in the evaluation of migrants’ land transfer-in intentions, external environmental factors have the greatest influence (with a weight coefficient of 0.7877), while individual characteristics (0.0486) and psychological characteristics (0.0593) have relatively low weight coefficients, indicating that migrants primarily rely on government policy support and lack internal motivation; (3) the land transfer-out intentions of surrounding villagers are most affected by farmland resource endowment (weight coefficient of 0.3284), indicating that the quality and quantity of land resources are key factors affecting villagers’ transfer-out willingness, while individual endowment factors have the smallest impact (weight coefficient of 0.1220). Three recommendations are proposed: stimulating migrants’ intrinsic motivation to enhance livelihood autonomy, protecting villagers’ land rights to increase transfer participation, and building a systematic land resource sharing model to promote sustainable resource allocation. This study provides theoretical support for optimizing the land transfer mechanism in resettlement areas, aiming to improve land use efficiency, support the livelihood transition of migrants, and offer practical insights for land management planning in poverty alleviation and resettlement projects in other countries. Full article

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects. Full article

In response to the evolving challenges of the integration and combination of multiple container terminal operations under berth water depth constraints, the multi-terminal dynamic and continuous berth allocation problem emerges as a critical issue. Based on computational logistics, the MDC-BAP is formulated to be a unique variant of the classical resource-constrained project scheduling problem, and modeled as a mixed-integer programming model. The modeling objective is to minimize the total dwelling time of linerships in ports. To address this, a Dueling Double DQN-based reinforcement learning algorithm is designed for the multi-terminal dynamic and continuous berth allocation problem A series of computational experiments are executed to validate the algorithm’s effectiveness and its aptitude for multiple terminal joint operation. Specifically, the Dueling Double DQN algorithm boosts the average solution quality by nearly 3.7%, compared to the classical algorithm such as Proximal Policy Optimization, Deep Q Net and Dueling Deep Q Net also have better results in terms of solution quality when benchmarked against the commercial solver CPLEX. Moreover, the performance advantage escalates as the number of ships increases. In addition, the approach enhances the service level at the terminals and slashes operation costs. On the whole, the Dueling Double DQN algorithm shows marked superiority in tackling complicated and large-scale scheduling problems, and provides an efficient, practical solution to MDC-BAP for port operators. Full article

The world’s demand for electrical energy is increasing rapidly while the use of fossil fuels is getting limited more and more by energy policies and the need for reducing the impact of climate change. New sources of energy are required to fulfill the world’s demand for electricity and they are currently found in renewable sources of energy, especially in solar and wind power. Choosing the optimal PV nominal power minimizes the unnecessary surplus of electrical energy that is exported to the grid and thus is not making any impact on the grid more than necessary. Oversizing the PV system according to the Croatian net-metering model results in switching the calculation of the costs to the prosumer model which results in a decrease of the project’s net present value (NPV) and an increase in the payback period (PP). This paper focuses on formulating and solving the optimization problem for determining the optimal nominal power of a grid-connected PV system with a case study for Croatia using multiple scenarios in the variability of electricity production and consumption. In this paper, PV systems are simulated in the power range that corresponds to a typical annual high-tariff consumption in Croatian households. Choosing the optimal power of the PV system maximizes the investor’s NPV of the project as well as savings on the electricity costs. The PP is also minimized and is determined by the PV production, household consumption, discount rate, and geographic location. The optimization problem is classified as a quadratically constrained discrete optimization problem, where the value of the optimal PV power is not a continuous variable because the PV power changes with a step of one PV panel power. Modeling and simulations are implemented in Python using the Gurobi optimization solver. Full article

Energy, water, and environment are inextricably interwoven in the complex social and economic networks. This study proposes an optimization model for planning the energy–water–environment nexus system (EWENS) through incorporating the linear autoregressive integrated moving average model prediction model (ARIMA), Monte Carlo simulation, chance-constrained programming (CCP), and type-2 fuzzy programming (T2FP) into one general framework. This method effectively tackles type-2 fuzzy set and stochastic uncertainties. The proposed model can quantitatively explore the interconnections between water, energy, and environment systems and generate an optimized solution for EWENS. The proposed model was applied to a coal-dominated region of China, i.e., Inner Mongolia. Several findings and policy implications were obtained. First, the total water supply for energy-generating activities will range from 1368.10 × 10⁶ m³ to 1370.62 × 10⁶ m³, at the end of planning periods. Second, the electricity for water supply will range from 2164.07 × 10⁶ kWh to 2167.65 × 10⁶ kWh at the end of the planning periods, with a growth rate of 46.06–48.72%. Thirdly, lifecycle carbon dioxide emission (LCDE) is projected to range from 931.85 × 10⁶ tons to 947.00 × 10⁶ tons at the end of the planning periods. Wastewater and SO₂, NOx, and particulate matter (PM) emissions are projected to be 42.72 × 10³–43.45 × 10³ tons, 183.07 × 10³–186.23 × 10³ tons, 712.38 × 10³–724.73 × 10³ tons, and 38.14 × 10³–38.80 × 10³ tons at the end of the planning periods. Fourthly, as the largest electricity-exporting city of China, Inner Mongolia’s electricity outflows will export 1435.78 × 10⁶ m³ of virtual water to other regions, implying that Inner Mongolia is pumping its important water resource to support other regions’ electricity demands. Finally, high carbon mitigation levels can effectively optimize the electricity power mix, reduce consumption amounts of water and coal, and mitigate air pollutants, wastewater, and LCDE. The obtained results provide useful information for managers to develop a sustainability plan for the EWENS. Full article

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO₂ fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO₂. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands. Full article

The purpose of this study was to examine two biomass district heating plants operating in Tuscany, with a specific focus on the ex-post evaluation of their economic and financial feasibility and of their environmental benefits. The former biomass district heating plant supplies only public users (Comunità Montana della Lunigiana, CML: administrative body that coordinates the municipalities located in mountain areas), the latter supplies both public and private users (Municipality of San Romano in Garfagnana). Ex-post investment analysis was performed to check both the consistency of results with the forecasts made in the stage of the project design and on the factors, which may have reduced or jeopardized the estimated economic performance of the investment (ex-ante assessment). The results of the study point out appreciable results only in the case of biomass district heating plants involving private users and fuelled by biomasses sourced from third parties. In this case, the factors that most influence ex-post results include the conditions of the woody biomass local market (market prices), the policies of energy selling prices to private users and the temporal dynamics of private users’ connection. To ensure the consistency of ex-post economic outcome with the expected results it is thus important to: (i) have good knowledge of the woody local market; (ii) define energy selling prices that should be cheap for private users but consistent with energy production costs and (iii) constrain private users beforehand to prevent errors in the plant design and in the preliminary estimate of return on investment. Moreover, the results obtained during the monitoring activities could help in providing information on the effectiveness of the supporting measures adopted and also to orient future choices of policy makers and particularly designers, to identify the most efficient configuration of district heating organization for improving energy and environmental performances of communities, and to develop a chain model for the optimization of energy use in the municipality. Full article

This paper presents an assessment of the cost of water scarcity in Cyprus, today and in the next 20 years, taking into account the effect of projected climate change in the region. It focuses on the residential sector, accounting also for tourism and industry. Using a simple demand function, total scarcity costs in Cyprus are computed for the period 2010–2030, and three scenarios of future water demand are presented. The central estimate shows that the present value of total costs due to water shortages will amount to 72 million Euros (at 2009 prices), and, if future water demand increases a little faster, these costs may reach 200 million Euros. Using forecasts of regional climate models, costs are found to be about 20% higher in a “climate change” scenario. Compared to the loss of consumer surplus due to water shortages, desalination is found to be a costly solution, even if environmental damage costs from the operation of desalination plants are not accounted for. Finally, dynamic constrained optimization is employed and shows that efficient residential water prices should include a scarcity price of about 40 Eurocents per cubic meter at 2009 prices; this would constitute a 30–100% increase in current prices faced by residential consumers. Reductions in rainfall due to climate change would raise this price by another 2-3 Eurocents. Such a pricing policy would provide a clear long-term signal to consumers and firms and could substantially contribute to a sustainable use of water resources in the island. Full article