Search Results (520)

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

Implementation of real-scale systems for rainwater harvesting, treatment and reuse of greywater in residential areas is challenging because several factors should be considered for full adoption and satisfaction of decision-makers, urban developers and users. Technological, construction, operational, social (acceptance), impact on water resources, regulatory, and economic factors are involved. This study presents the implementation of a full-scale hybrid system for rainwater harvesting, treatment and reuse of greywater in a residential building located in Monterrey, Nuevo León, Mexico. The study included intervening in the hydraulic infrastructure of an already constructed residential building for collecting greywater, harvesting and collecting rainwater, designing and constructing an 80 m² controlled natural soil treatment system (CNSTS) and a 65 m³ storage tank for treating and storing rain and greywater. Furthermore, the full-scale hybrid system was monitored under real operating conditions for a two-month period to assess its performance. Results showed that the CNSTS has the potential to replace up to 2835 m³ year⁻¹ of potable water, equivalent to 65% of the building’s annual water consumption. The CNSTS achieved removal efficiencies of up to ~90% for Chemical Oxygen Demand, 90% for surfactants, and 50% for total nitrogen. Most of the measured parameters complied with the corresponding limits established by the Mexican standards NOM-003-SEMARNAT-1997 for non-potable water reuse, NOM-001-SEMARNAT-2021 for wastewater discharges, and NOM-127-SSA1-2021 for potable water with the exception of methylene blue active substances (surfactants), which exceeded the permissible limit during the initial monitoring stage, highlighting the need for further optimization of the system’s vegetative cover. Based on these findings, conceptual designs and preliminary evaluations were conducted for additional buildings, resulting in potable water substitution rates above 90% with investment payback periods of 2 to 5 years, depending on the water demand and the water catchment potential. Full article

Against the backdrop of a polycrisis shaped by the interaction of climate change, resource constraints, and digital transformation, urban ecological welfare performance (EWP) captures cities’ ability to achieve higher welfare outcomes while limiting resource consumption and environmental costs. It also indicates their capacity to sustain green development and welfare provision under multiple pressures. Using panel data for 242 prefecture-level cities in China from 2008 to 2023, this study measures urban EWP with a super-efficiency slacks-based measure (SBM) model. It then treats China’s national smart city pilot policy as a quasi-natural experiment and applies a multi-period difference-in-differences (DID) model to estimate the effect of smart city construction (SC) on EWP. The results indicate that SC significantly improves EWP, and this finding remains robust across a range of sensitivity tests. Mechanism analyses provide evidence consistent with resource allocation optimization and green technological innovation as two potential channels. Relatively larger estimates are observed for cities in northeastern China, large cities, and non-resource-based cities. The interaction estimates further suggest that greater fiscal investment intensity and higher internet penetration strengthen the positive relationship between SC and EWP. These findings support the context-sensitive implementation of smart city strategies and coordinated improvements in ecological sustainability and residents’ welfare. Full article

This study aims to identify and evaluate the impact of operational and organizational activities on reducing energy consumption in selected stages of agricultural transport equipment component production. The research focused on manufacturing companies from the agricultural machinery sector, characterized by high energy intensity and operational complexity. The empirical study was conducted among 121 experts representing manufacturing enterprises. The results indicate that enterprises achieve the highest level of implementation in organizational and Lean Manufacturing-related activities, particularly in reducing downtime, optimizing production scheduling, limiting empty transport runs, and eliminating overproduction. In contrast, technological and investment-intensive solutions, such as heat recovery systems, advanced energy monitoring, and digital simulation tools, remain implemented to a significantly lower extent. The findings also reveal a moderate level of maturity in energy management practices and limited integration of energy-related data into strategic decision-making processes. The study confirms the multidimensional nature of energy efficiency and highlights the importance of integrating Lean Manufacturing principles with digital technologies and systemic energy management. The proposed research model may serve as a practical diagnostic tool supporting the identification of key improvement areas for sustainable production development in manufacturing enterprises. This study aims to identify and evaluate the impact of operational and organizational activities on reducing energy consumption at selected stages of agricultural transport equipment component manufacturing. The research focused on manufacturing companies operating in the agricultural machinery sector, which is characterized by high energy intensity and operational complexity. The empirical study involved 121 experts representing manufacturing enterprises. The results indicate that organizational and Lean Manufacturing-related practices exhibit the highest levels of implementation, particularly those aimed at reducing downtime, optimizing production scheduling, limiting empty transport runs, and eliminating overproduction. In contrast, technology-intensive and capital-intensive solutions, such as heat recovery systems, advanced energy monitoring, and digital simulation tools, show substantially lower implementation levels. The findings also reveal a moderate level of maturity in energy management practices and limited integration of energy-related data into strategic decision-making processes. The findings confirm the multidimensional nature of industrial energy efficiency and highlight the importance of integrating Lean Manufacturing principles with digital technologies and systematic energy management. The proposed research model may serve as a practical diagnostic tool for identifying key areas for improvement in the development of sustainable manufacturing. Full article

To quantify the potential of carbon pricing and green finance to jointly drive deep decarbonization in the building sector, this paper extends the MESSAGEix-Buildings model using a policy-endogenous approach. It incorporates the internalization of carbon pricing and carbon emission costs, as well as mechanisms to relax capital constraints in green finance, into the system optimization framework, thereby enabling the quantification of marginal abatement costs and policy interactions. Using civil buildings in Beijing as case studies, four scenarios were devised for simulation analysis. The results show that: (1) carbon pricing alone can achieve a 11.44% reduction in carbon emissions by 2050, whereas green finance can only alleviate investment barriers and deliver only modest additional emission reductions; (2) when the two policies are implemented in tandem, the joint framework generates complementary price constraints and financial incentives, achieving an additional 4.68% reduction in final energy consumption and a nearly 5.27% saving in total investment compared with the carbon-pricing-only scenario. The study elucidates the complementary mechanism of ‘price constraints and cost incentives’, highlighting the methodological value of an endogenous policy approach, and provides quantitative support for the implementation of carbon neutrality in construction and green finance in megacities. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

This study extends an established bi-level dynamic optimization framework for grid-connected regional integrated energy systems (RIES) to address the escalating renewable energy penetration (REP) within integrated power systems (IPS). While traditional models treat REP as static, our approach integrates its dynamic growth into both the design and operational scheduling phases, utilizing a genetic algorithm paired with the Gurobi solver. Applied to a case study in Changsha, the extended model is systematically benchmarked against conventional static REP scenarios. The research shows that the introduction of REP growth factors in the optimization model can increase the installation capacity of ground source heat pumps (GSHPs), reduce the installation capacity of combined heat and power units and absorption chillers, and reduce the initial investment of the system by 12.20%. Affected by the difference in equipment capacity configuration, the primary energy consumption and total cost of the grid-connected RIES decrease during the planning period, while the cumulative carbon dioxide emissions show a slight increase. The primary energy consumption and total cost under winter operating conditions were reduced by 2.44% and 2.38%, respectively. In addition, with the increase of REP growth rate in IPS, the reductions in the system’s primary energy consumption, carbon dioxide emissions, and total cost all increase accordingly. Therefore, in macroscopic RIES planning, incorporating dynamic REP reveals a clear trade-off: improving economic and energy efficiency may temporarily increase carbon emissions when renewable penetration is low. Full article

We study an infinite-horizon consumption, portfolio, and early-retirement problem for a wage earner who receives labor income while working but bears a constant utility cost of labor. Retirement is irreversible and removes both labor income and work disutility. In addition, retirement is feasible only after the agent has accumulated a prescribed level of financial wealth. We solve the problem under constant relative risk aversion using a dual martingale method. The post-retirement problem reduces to the standard Merton problem, while the pre-retirement problem becomes an optimal stopping problem with a target-induced obstacle. We derive an explicit dual value function, characterize the free boundary, recover the primal value, and obtain closed-form consumption, portfolio, and retirement policies. The solution exhibits a sharp slack–binding dichotomy: small targets do not affect the classical disutility retirement policy, whereas large targets become the effective retirement threshold and reshape both consumption and risky investment before retirement. Full article

Environmental pollution is becoming an urgent global issue. Countries are implementing various solutions and establishing legal frameworks and institutional policies to promote the use of green energy sources and thereby minimize the adverse environmental impact of economic growth, investment, and consumption. The goal of economies worldwide, including in Asia, is to implement policies that promote sustainable development while optimizing economic performance. The purpose of this study is to evaluate the impact of green energy consumption and institutional quality on environmental quality. Using data from 29 selected Asian countries over the period 1970–2021, the empirical results indicate that green energy consumption does not have a statistically significant impact on environmental quality across the full sample. Improvements in institutional quality can enhance environmental quality and accelerate progress toward carbon neutrality and net-zero emissions targets. The Climate Change Conferences (COPs) have helped raise global awareness of the dangers of climate change, fostering greater international cooperation to protect the planet and promote sustainable development. To capture the influence of international climate commitments, this study employs a dummy variable for the COP period (coded 0 before COP2 in 1996 and 1 thereafter). This variable is interacted with green energy consumption and institutional quality to examine whether the implementation of COP commitments moderates their effects on CO₂ emissions. The empirical results show that greater political stability, particularly during the implementation of carbon emission reduction commitments, contributes to reducing greenhouse gas emissions and promoting sustainable development. The study also confirms that institutional quality plays a significant role in improving environmental quality and accelerating countries’ progress toward carbon neutrality and net-zero emission goals. Full article

In this paper, we study a class of mean-field singular stochastic optimal control problems for systems governed by regime-switching mean-field stochastic differential equations. The state dynamics depend on both regular and singular controls, and the coefficient of the singular component is allowed to depend explicitly on the state variable. We establish both necessary and sufficient stochastic maximum principles for this class of problems under the assumption that the control domain is convex. The presence of the state variable in the singular term leads to an adjoint process characterised by a generalised backward stochastic differential equation. As an application, we consider a one-dimensional regime-switching mean-field portfolio optimization problem with transaction costs, where the investor controls consumption and cumulative investment in a risky asset. Full article

Renewable energy communities (RECs) are increasingly recognized as a strategic instrument for enhancing the sustainability and resilience of energy systems, promoting local renewable integration, and reducing consumer exposure to electricity market volatility. This study analyzes the Italian electricity market and assesses the economic performance of RECs relative to individual consumers using high-frequency hourly data from 2021 to 2023, covering both the 2022 European energy crisis and the subsequent Italian regulatory reform of incentive mechanisms. The optimization problem is formulated in physical terms, aiming to maximize locally utilized energy, defined as the sum of self-consumed and shared photovoltaic generation. This choice reflects the structure of the Italian regulatory framework, where incentives are directly linked to the amount of energy shared within the community. In this context, energy-based optimization is preferred to avoid embedding assumptions on discount rates, investment horizons, and financing conditions, which may vary significantly across users and introduce additional uncertainty. From a sustainability perspective, maximizing local energy utilization contributes to improving energy efficiency, reducing reliance on external energy sources, and enhancing the capacity of decentralized systems to absorb market shocks. For this reason, economic indicators such as Net Present Value (NPV) or payback period are not explicitly included in the optimization objective. This is justified by the focus of the analysis on short-term operational performance and exposure to electricity price volatility, rather than long-term investment evaluation. Moreover, given that the economic value of the REC is largely determined by shared energy volumes under the current Italian incentive scheme, maximizing local energy utilization provides a consistent proxy for economic performance. Nevertheless, the integration of financial metrics such as NPV or payback period represents a relevant extension for future research, particularly in the context of investment decision-making. Through panel econometric analysis, we estimate the sensitivity of economic value to electricity price fluctuations. Results show that RECs reduce price sensitivity by approximately 8–15% compared to individual users, as estimated by panel regression coefficients. Furthermore, the volatility of economic value decreases by around 1.95% under the community configuration, particularly during the 2022 price shock demonstrating that RECs exhibit significantly lower price dependence than standalone consumers. To assess the robustness of these findings, a machine learning framework is employed to relax linearity assumptions and capture potential non-linear effects. Results consistently show that while market prices remain an important determinant, RECs substantially attenuate their impact, particularly during periods of extreme price stress. A policy counterfactual comparison between pre- and post-reform incentive structures further indicates that the coefficient of variation decreases by approximately 4.4% under the post-reform incentive scheme, highlighting the role of policy design in supporting economically and operationally sustainable energy communities. Overall, this study develops a data-driven analysis based on a high-frequency synthetic dataset designed to reproduce realistic consumption and generation dynamics, providing robust evidence that RECs contribute not only to renewable energy deployment but also to the economic and systemic sustainability of electricity markets under conditions of high volatility. Full article

Artificial intelligence (AI) may raise productivity by automating tasks, augmenting human work and reducing information-processing costs. Yet productivity gains are not necessarily converted into broadly shared purchasing capacity or fully absorbed output. This conceptual review develops the notion of the Distributional Absorption Threshold of AI-Induced Productivity, defined as the point at which AI-related productivity growth outpaces the growth of broadly distributed real purchasing power and household consumption. The framework links AI-induced productivity to labour income, income distribution, prices, investment, fiscal redistribution, external demand and effective demand. It distinguishes a favourable transmission path, in which productivity gains support wages, disposable income, consumption and output absorption, from a critical path, in which weak distributive transmission may generate absorption tension. The review formulates conceptual propositions and preliminary operational indicators for future empirical research while treating the threshold as an analytical construct rather than a fixed empirical constant. Its contribution is theoretical: it reframes the AI productivity debate beyond both technological optimism and automation anxiety by connecting technological change, distribution and demand-side realisation. Full article

With the strategic advancement of energy structure transformation and the implementation of carbon peaking and carbon neutrality goals, the Integrated Energy System (IES) has become a core research direction owing to its superior performance in multi-energy complementation, operational efficiency, and low-carbon emission characteristics. Nevertheless, existing studies reveal that the optimal operation of IES still faces significant challenges, including the high complexity of multi-energy coupling, supply–demand imbalance caused by renewable energy penetration, and insufficient exploitation of demand-side flexibility. As a core measure of demand-side management, demand response (DR) provides an effective approach to motivate users to adjust power load via price incentives or direct load control. DR can effectively smooth load profiles, improve resource utilization, and boost the consumption level of renewable energy. To meet the operational demands of modern IES, this paper establishes a security-constrained economic dispatch model embedded with multi-level demand response mechanisms. The proposed framework is divided into four key modules: First, a price-based demand response strategy is developed to dynamically guide users in regulating multi-energy consumption behaviors. Second, electric vehicles (EVs) are considered flexible demand-side resources with unique response characteristics. An aggregated EV charging–discharging model is established to suppress power fluctuations and support high proportions of renewable energy integration. Third, to precisely calculate the overall operating cost of IES, a combined economic evaluation index integrating time-of-use tariff and Levelized Cost of Electricity is adopted. It maintains a balance between amortized long-term generation investment and short-term operational expenditure, and coordinates the economic benefits and operational reliability of the whole system. Finally, numerical simulations are performed on a coupled test system comprising an IEEE 33-bus distribution network and a 20-node natural gas network. Simulation results verify that the proposed co-optimization model can effectively reduce total system operating costs and greatly improve the local assumption of fluctuating renewable energy. Full article

This paper extends a benchmark reversible job-choice framework by imposing a subsistence-consumption constraint. The agent chooses consumption, portfolio allocation, and one of two jobs in continuous time. The first job provides low income and high leisure, whereas the second job provides high income and low leisure. We focus on the case in which the coefficient of relative risk aversion satisfies $\gamma >1$, so that the transformed risk-aversion parameter also satisfies ${\gamma }_1>1$. Consumption must satisfy the lower bound $c_t\ge \underline{c}$ under both jobs. The constraint changes both the natural solvency boundary and the job-switching rule. In the dual problem, each job generates a subsistence-adjusted reward whose form depends on whether the consumption floor is binding. The optimal job is selected by the upper envelope of the two dual rewards. We prove that, when $\gamma >1$, the resulting dual switching function has a unique positive zero. We also characterize the location of this zero explicitly in terms of the income gap $Y_1-Y_0$ and the subsistence level $\underline{c}$. Hence the optimal job policy remains a one-threshold rule: the agent chooses the high-income job at low wealth and the high-leisure job at high wealth. The consumption floor shifts this boundary and implies that near the solvency boundary the agent necessarily consumes at the subsistence level and works in the high-income job. We provide a complete closed-form representation of the dual value function in all possible regimes. Full article

The increasing penetration of variable renewable energy sources (RESs) poses significant challenges to power system flexibility and reliability, particularly in systems with high solar generation. At the same time, existing Concentrating Solar Power (CSP) plants in Europe face declining economic viability due to high capital costs and the expiration of incentivized tariff schemes. This study proposes and evaluates a novel approach to repurpose CSP plants as flexible energy assets through the integration of cold thermal energy storage (CTES) within a Thermally Integrated Power-to-Heat-to-Power Energy Storage (TI-PTES) framework. The proposed system combines an ice/water-based cold storage with a CO₂-based refrigeration cycle to enhance the efficiency of the CSP steam cycle by reducing condenser temperatures, while also enabling temporal shifting of electricity consumption. A techno-economic optimization model based on PyPSA is developed to determine the optimal sizing and operation of the storage and refrigeration system under realistic load and electricity price conditions representative of the Spanish market. Results show that the integration of cold storage significantly alters system operation, shifting the chiller from a continuous demand-following mode to an intermittent, high-intensity regime. This leads to a reduction in annual operating expenditures by approximately 32% and an increase in annual profit and net present value (NPV), despite higher capital investment. While hourly net revenue becomes more volatile, with negative values during charging periods, cumulative annual performance improves due to effective temporal optimization. However, the absence of strong electricity price arbitrage and negative price signals limits the revenue potential of the storage system, which primarily acts as a cost-reduction mechanism. The findings demonstrate that cold thermal storage can successfully reposition CSP plants as flexible, value-generating assets in modern electricity systems. The proposed concept offers a promising pathway for extending the operational lifetime of existing CSP infrastructure while supporting higher integration of renewable energy sources. Full article