Search Results (114)

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

In China, the recycling system for waste tires is characterized by high output but low standardized recovery rates. This study examines the environmental and health risks caused by non-compliant treatment by individual recyclers and explores the barriers to the large-scale adoption of Pyrolysis Technology. A Tripartite Evolutionary Game Model involving pyrolysis plants, waste tire recyclers, and government regulators is developed. The model incorporates pollutants from pretreatment and pyrolysis processes into a unified metric—Carbon Dioxide Equivalent (CO₂-eq)—based on Global Warming Potential (GWP), and designs a Differential Carbon Taxation mechanism accordingly. The strategy dynamics and stability conditions for Evolutionary Stable Strategies (ESS) are analyzed. Multi-scenario numerical simulations explore how key parameter changes influence evolutionary trajectories and equilibrium outcomes. Six typical equilibrium states are identified, along with the critical conditions for achieving environmentally friendly results. Based on theoretical analysis and simulation results, targeted policy recommendations are proposed to promote standardized waste tire pyrolysis: (1) Establish a phased dynamic carbon tax with supporting subsidies; (2) Build a green market cultivation and price stabilization system; (3) Implement performance-based differential incentives; (4) Strengthen coordination between central environmental inspections and local carbon tax enforcement. Full article

Considering consumers’ low-carbon preferences, this article analyzes a manufacturer’s price and carbon abatement strategies, as well as a retailer’s price and promotion strategies, in a centralized game, where the manufacturer and the retailer jointly make decisions, and a decentralized game, where the two parties each make decisions simultaneously. This study discusses the impact of the carbon abatement cost coefficient, promotion cost coefficient, sensitivity coefficient of consumer demand to carbon abatement rate or promotion rate, or carbon tax rate on the manufacturer’s carbon abatement rate, commodity’s retail price, and retailer’s promotion rate. This article also discusses the impact of any one of the main parameters on supply chain profit. Through comparisons of the above two games, this article concludes that the former is better than the latter for firms, consumers, and the environment. This article also concludes that a reduction in the carbon abatement cost coefficient, a rise in the sensitivity coefficient of consumer demand to the carbon abatement rate, or a rise in the carbon tax rate increases the manufacturer’s optimal carbon abatement rate. A relatively high carbon abatement rate means relatively low CO₂ emissions, which are environmentally friendly and conducive to sustainable development at the ecological level. The foregoing conclusions provide governments with references for making carbon tax policies and also offer firms references for making decisions. Full article

Environmental sustainability is receiving growing global attention, making the development of low-carbon and green transportation increasingly important. Low-carbon policies offer significant advantages in incentivizing energy conservation and reducing emissions in the transportation sector; however, it is vital to consider the impacts of regional differences on the implementation effect of low-carbon policies. This paper explores multimodal transportation route optimization under a carbon tax policy. First, a bi-objective route optimization model is constructed, with the goal of minimizing total transportation cost and time, while accounting for uncertain demand, fixed departure schedules, and regional differences. Trapezoidal fuzzy numbers are used to represent uncertain demand, and a fuzzy adaptive non-dominated sorting genetic algorithm is designed to solve the bi-objective optimization model. The algorithm is then tested on differently sized networks and on real-world transportation networks in eastern and western China to validate its effectiveness and to assess the impacts of regional differences. The experimental results show the following. (1) When considering transportation tasks at different network scales, the proposed fuzzy adaptive non-dominated sorting genetic algorithm outperforms the NSGA-II algorithm, achieving minimum differences in percentages of cost and time of 9.25% and 7.72%, respectively. (2) For transportation tasks assessed using real-world networks in eastern and western China, an increase in the carbon tax rate significantly affects carbon emissions, costs, and time. The degree of carbon emission reduction varies depending on the development of the regional transportation network. In the more developed eastern region, carbon emissions are reduced by up to 44.17% as the carbon tax rate increases. In the less developed western region, the maximum reduction in carbon emissions is 14.37%. The carbon tax policy has a more limited impact in the western region compared to the eastern one. Therefore, formulating differentiated carbon tax policies based on local conditions is an effective way to maximize the economic and environmental benefits of multimodal transportation. Full article

Palm oil production generates substantial underutilized biomass wastes, including empty fruit bunches, fiber, palm kernel shells, and palm oil mill effluent (POME). Waste-to-energy systems offer a viable pathway to convert these residues into electricity and fertilizer, supporting circular economy goals and sustainability targets. This study takes an example of palm oil waste from the Indragiri Hulu region in Riau Province in Indonesia. It develops a multi-objective optimization framework to evaluate palm oil mill WtE systems from economic, environmental, and energy output. Three scenarios are analyzed: maximal profit (MP), maximal profit with carbon tax (MPCT), and all waste processing (AWP). The MP scenario favors high-return technologies such as gasification and incineration, leading to significant greenhouse gas emissions. The MPCT scenario favors lower-emission technologies like composting and excludes high-emission, low-profit options such as POME digestion. In contrast, the AWP scenario mandates the processing of all wastes, leading to the lowest profits and the highest emissions among all scenarios. The sensitivity analysis reveals that POME processing is not feasible when electricity prices are below the government-set rate, but becomes viable once prices exceed this threshold. These findings offer valuable insights for companies and policymakers seeking to develop and implement effective strategies for optimal waste utilization. Full article

This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver’s attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver’s requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation. Full article

The increasing human demand for natural resources is leading to critical resource depletion. This depletion is exacerbated by exceeding the Earth’s biological regeneration rate, threatening ecosystems’ ability to renew biomass. This ecological challenge hinders the potential for simultaneous economic, social, and environmental progress. This study investigates the complex relationships between the USA’s per capita income, energy efficiency innovations, environmental taxation, political stability, and its biocapacity. Using annual data from 1990 to 2024, the paper employs a comprehensive causality testing framework that accounts for the nonlinear nature of the data, as asymmetric effects are observed. This framework includes the Quantile Autoregressive Distributed Lags model (Q-ARDL), the Wald test for parameter consistency, and the Granger-causality in Quantiles test (GC-Q), enabling the estimation of unique parameter vectors for each quantile. A key finding reveals that the impact of per capita GDP on biocapacity is significantly larger than that of other regulatory mechanisms. This suggests that carbon pricing and energy efficiency technologies require widespread implementation to offset the environmental impact of economic growth. The quantile regression reveals complex short-run impacts on biocapacity with persistent positive effects from its lag, contrasting with the diminishing negative influence of GDP and positive influence of energy efficiency at higher quantiles, while long-run analysis shows a consistent negative impact of GDP and varying positive or nonlinear effects of other factors. Granger-causality tests indicate significant unidirectional positive effects from energy efficiency and political stability to biocapacity, a bidirectional relationship for environmental taxes in upper quantiles and GDP across all quantiles. The associated methodological and policy implications aim to assist policymakers in achieving a better balance between the benefits and costs of natural resource use in the USA, promoting sustainable development. Full article

This paper uses a difference-in-differences approach to investigate how China’s water resources tax reform influences corporate Environmental, Social and Governance (ESG) performance. Drawing on a panel dataset of A-share listed companies from 2013 to 2023, we find that the reform significantly improves firms’ ESG ratings, a result that holds under multiple robustness checks. Mechanism tests reveal that this positive effect operates through enhanced green technological innovation, increased environmental investment, and heightened pressure from capital markets, with media attention further reinforcing these pathways. Heterogeneity analyses indicate that state-owned enterprises and larger firms experience particularly strong ESG improvements following the tax reform. These findings provide empirical evidence of the effectiveness of government-led environmental governance policies and offer practical insights for promoting green transformation in the corporate sector. Full article

Carbon capture, utilization, and storage (CCUS) technology is an essential technology for achieving low-carbon transformation and upgrading of the coal power industry. This study applies optimal control theory to analyze the dynamic optimization of CCUS technological innovation investment in coal power stations under environmental protection tax. A dynamic control model is constructed to analyze the investment decisions of firms at system steady-state equilibrium, and numerical simulations are performed. The study shows that under both profit maximization and social welfare maximization conditions, a distinct saddle-point steady-state; the environmental protection tax affects technological innovation investment in coal power stations, which in turn affects electricity prices; the learning rate of knowledge accumulation also impacts technological innovation investment: under the social welfare maximization condition, the investment levels in technological innovation, technology, and knowledge accumulation are higher than those under profit maximization. Full article

The adoption of electric vehicles (EVs) in Thailand has been growing steadily. This research study investigated the Thai automotive industry cluster and completed an analysis of the EV transition to create a domestic industry, its social and environmental impacts, the laws supporting EVs, and consumer acceptance. The results of the study found that Thailand had 1.53 million EVs, accounting for 8.12% of all vehicles in 2023. This growing industry has a significant impact on Thailand’s GDP, highlighting the need for a strategic transition to support the development of the EV conversion industry. A particular focus is on the conversion of internal combustion engine vehicles that are over 10 years old. These debt-free vehicles, having simpler technology, present an ideal opportunity for modification as part of a broader strategy to prepare the domestic automotive sector for EV production and usage. A strong legal framework supports Thailand’s efforts by promoting the EV sector and EV conversion. The country is already a major automotive manufacturing hub, ranked 11th globally, with a robust automotive parts industry. Additionally, Thailand has a significant stock of older and classic cars, with 18.26% of its vehicles in 2023 being over 20 years old. The absence of laws restricting vehicle lifespan further facilitates the EV conversion process. Thailand’s legal measures also promote economic growth by supporting industries related to classic cars, with tax incentives designed to stimulate these sectors. The EV conversion business in Thailand attracted over USD 300 million in investment in 2023, with a survival rate of 97.33%, reflecting continuous year-on-year growth. These developments indicate that Thailand is well positioned to become a leading hub for EV conversion in Southeast Asia. If Thailand wants the EV conversion industry to grow without affecting stakeholders, those involved must consider the following issues or develop a transition strategy: (1) investment in the EV conversion industry; (2) environmental aspects of EV conversion; (3) laws related to EVs; and (4) consumers who are considering EV conversion. Countries with automotive industries similar to Thailand can consider using this model in their countries as well. Full article

Scientific evidence shows that human population and economic activity are nearing or exceeding the limits of global environmental systems. In response, many countries, including China, have introduced regulations to mitigate this pressure. China’s Ecological and Environmental Damage Compensation System (EDCS), launched in 2015, emphasizes valuing ecological functions and flexible compensation. This study analyzes the EDCS’s impact on industrial wastewater discharge using prefecture-level data (2012–2021) and a time-varying difference-in-differences (DID) method. The results show that EDCS significantly reduces industrial wastewater discharge, particularly in small cities, lightly polluted areas, and Central China. However, increased public spending on science and technology and extended drainage infrastructure weaken this effect. Further analysis shows that the EDCS enhances centralized sewage treatment rates, contributing to better control of water pollution. This study highlights the system’s effectiveness in water pollution management and emphasizes the importance of internalizing the value of ecological functions in policy design. It urges policymakers to enhance the system’s implementation efficiency, introduce tax incentives to promote green investment, and leverage market mechanisms to ensure long-term environmental sustainability. Full article

The adoption of electric motorcycles is critical for reducing transportation-related greenhouse gas emissions in Indonesia, which reached 674.54 million t of CO₂ in 2023. This study integrates the Theory of Planned Behavior with situational, contextual, and demographic factors to explore the determinants of electric motorcycle adoption intentions and actual usage. Data were collected from 1602 respondents across ten provinces with the highest motorcycle sales using purposive sampling and analyzed through Partial Least Squares—Structural Equation Modeling. Findings reveal that psychological factors—attitude, subjective norms, and perceived behavioral control—significantly influence purchase intentions, while personal moral norms do not. Situational factors such as technology and cost indirectly affect adoption intentions through attitude and perceived behavioral control. Contextual factors show mixed results; government policies effectively shape attitudes and perceived behavioral control, but infrastructure remains inadequate to influence attitudes directly. Demographic analysis highlights gender as a moderating factor, with men showing higher moral-driven adoption intentions. These results imply that the government and manufacturers need to develop the appropriate strategy to foster public interest in adopting electric motorcycles to increase the adoption rate of pro-environmental vehicles. Government policies such as purchase price subsidies, tax reductions, and charging rate discounts can motivate the intention to adopt electric motorcycles. In addition, manufacturers could improve technical performance and reduce the total cost of ownership, such as the purchase price and battery replacement costs. Full article

Currently, the UK has ambitious plans to reach net zero by 2050, despite other countries such as Russia and India targeting 2060 and 2070, respectively. Assuming that the UK emissions unceasingly decline at a given rate annually towards achieving net zero by 2050, its economy would need to ensure a reduction of 105 MtCO2 per year of its emissions from the current 2021 levels. Given that global greenhouse gas emissions have not peaked and continue to rise, the UK seeks to implement costly and aggressive emission reduction policies towards fulfilling commitments under the 2021 Glasgow Climate Pact. This paper investigates the effect of environmental taxes on environmental degradation in the UK between 2000Q1 and 2019Q4 using novel Fourier approaches. Using the novel Fourier ARDL estimator, the long-run equilibrium estimates indicate that gross domestic product and environmental tax cause a fall in carbon emissions. However, in trade and primary energy use, a unit change caused rising carbon emissions in the UK. Especially, the results indicate that environmental taxes have a negative effect on environmental degradation in the UK, and ecological tax policy could be considered as an effective channel to attain environmental sustainability. The outcome provides the following policy insights: (i) The government of the UK should support international environmental tax coordination mechanisms, especially on carbon pricing, to avoid relocation of carbon-intensive investments. (ii) The UK government must note that imposing more taxes to encourage emissions reductions could bring complexity to the tax system and unnecessarily bring costly ways to deal with climate change. Higher domestic electricity prices could disproportionately hit low-income households and create distributional cost concerns, which require benefit payouts or compensation schemes. (iii) Switching to electric vehicles simultaneously requires investments in charging infrastructure and battery technologies. To avoid this chicken-and-egg problem, the government of the UK could play a coordinating role, including deploying targeted subsidies, regulations, direct government involvement, or setting higher carbon prices in special cases. Full article

The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods. Full article

Global warming caused by greenhouse gas emissions has become a worldwide environmental problem, posing a great threat to human survival. As the world’s largest emitter of carbon dioxide, China has pledged to reach peak carbon emissions by no later than 2030 and carbon neutrality by 2060. It is found that a carbon tax is a powerful incentive to reduce carbon emissions and promote an energy revolution, but it may have negative socio-economic impacts. Therefore, based on China’s 2020 input–output table, this paper systematically investigates the impacts of a carbon tax on China’s economy, carbon emissions, and energy by applying a computable general equilibrium model to determine the ideal equilibrium between socio-economic and environmental objectives. Based on energy use characteristics, we subdivided the energy sector into five major sectors: coal, oil, natural gas, thermal power generation, and clean power. The results show that when the carbon emission reduction target is less than 15%, that is, when the equilibrium carbon tax price is less than 54 yuan/ton, the implementation of a carbon tax policy can significantly reduce carbon emission and fossil fuel energy consumption, while only slightly reducing economic growth rate, and can achieve the double dividend of environment and economy. Moreover, because the reduction of coal consumption has the greatest impact on reducing carbon emissions, the ad valorem tax rate on coal after the carbon tax is imposed is the highest because coal has the highest carbon emission coefficient among fossil fuels. In addition, as an emerging clean energy source, hydrogen energy is the ideal energy storage medium for achieving clean power generation in power systems. If hydrogen energy can be vigorously developed, it is expected to greatly accelerate the deep decarbonization of power, industry, transportation, construction, and other fields. Full article