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Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Despite the importance of tax policy in reducing energy consumption and carbon emissions, there is a dearth of research on the environmental impact of indirect taxes. This paper examines the impact of indirect taxes on carbon dioxide (CO₂) emissions, with an emphasis on institutional quality.

The study uses the Government Revenue Dataset (2021), comprising 143 countries, dividing into 114 developing and 29 developed countries, during the period between 1996 and 2019. The author adopts panel data techniques, with Driscoll–Kraay standard errors to account for the issue of cross-sectional dependence (CSD).

The results indicate that indirect tax revenues have a negative and significant impact on CO₂ emissions for the total sample. The subsample analysis revealed that while indirect taxes reduce carbon emissions in developing countries, opposed results are reported for developed countries. This finding implies that most of the advanced countries have already reached a high level of taxes, at which carbon emissions increase as indirect tax increases further. Interestingly, the results revealed that institutional quality enhances the role of indirect taxes in mitigating carbon emissions for both developing and developed countries.

To the best of the authors' knowledge, this is the sole study using the newly developed tax data by the United Nations University, World Institute for Development Research (UNU-WIDER) to investigate the impact of indirect taxes on carbon emissions, with an emphasis on institutional quality. The existing literature focuses on specific taxes, like carbon taxes, with no comprehensive research on the link between indirect taxes and carbon emissions.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

This study aims to explore the relationship between the degree of state-owned enterprises’ (SOEs) mixed reform and the environmental response of enterprises, against the background of actively promoting the reform of mixed ownership in China.

The study is conducted on a sample of A-share listed manufacturing companies in Shanghai and Shenzhen of China, investigated for the period 2015 to 2020. The baseline regression results are robust to a series of robustness and endogeneity tests. To deal with the issue of endogeneity, the technique of instrumental variable method has been applied.

The study confirms the U-shaped effect of the depth and restriction of mixed ownership on SOEs’ environmentally responsive behaviour in the manufacturing industry, especially for lower environmental regulation and higher level of risk-taking firms. The findings indicate that the government, shareholders and other stakeholders of enterprises should not simply consider that the mixed reform is directly promoting or reducing the environmental response behaviour of enterprises.

SOEs should improve their shareholding structures to undermine performance enhancement at the expense of the environment and increase environmentally beneficial behaviours. Regulators and governments should improve the institutional mechanism of environmental regulation and make efforts to promote corporate awareness of the environment.

Although the adoption and implementation of environmentally friendly policies are costly, improved environmental response and other social responsibilities are helpful to corporate long-term growth and reputation and obtain more capital market attention. Therefore, firms would benefit from improving their environmental response to protect nature, as well as to enjoy the economic and social benefits of a better environmental response.

To the best of the authors’ knowledge, there is a lack of studies focussing on the environmental behaviour of SOEs of mixed reform. As the mixed reform in China has come to a climax phase in recent several years, SOEs of mixed reform is an ideal environment for research. The study focusses on manufacturing firms as these firms are more susceptible to contribute to environmental pollution, exploitation of natural resources and labour concerns.

The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure.

A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration.

The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%.

As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently.

This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

This study is intended to analyze the impact of information and communication technology (ICT) infrastructure, technological innovation, renewable energy consumption and financial development on carbon dioxide emissions in emerging economies.

The present study adopts the autoregressive distributed lag (ARDL) cointegration technique for the annual data collection of Vietnam from 1990 to 2020.

The results of the study unveil that renewable energy consumption, the interaction between renewable energy consumption and ICT infrastructure and financial development have significant predictive power for carbon dioxide emissions. In the long term, renewable energy consumption, export and population growth reduce CO2 emissions, whereas the interaction between renewable energy consumption and ICT infrastructure and financial development increases CO2 emissions, while ICT infrastructure does not affect emissions. In the short run, changes in ICT infrastructure contribute to carbon dioxide emissions in Vietnam. In addition, changes in renewable energy consumption, financial development, the interaction between ICT infrastructure and renewable energy consumption and population growth have a significant effect on CO2 emissions. Notably, technological innovation has no impact on CO2 emissions in both the short and long run.

The current study provides new insights into the environmental effects of ICT infrastructure, technological innovation, renewable energy consumption and financial development. The interaction between renewable energy consumption and ICT infrastructure has a significant effect on carbon dioxide emissions. The paper suggests important implications for setting long-run policies to boost the effects of financial development, renewable energy consumption and ICT infrastructure on environmental quality in emerging countries like Vietnam in the coming time.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

This paper considers carbon abatement in a competitive supply chain that is composed of a manufacturer and two retailers under vertical shareholding. The authors emphasize the equilibrium decision problem of stakeholders under vertical shareholding and different power structures.

A game-theoretic approach was used to probe the influence of power structure and retailer competition on manufacturers' carbon abatement under vertical shareholding. The carbon abatement decisions, environmental imp4cacts (EIs) and social welfare (SW) of different scenarios under vertical shareholding are obtained.

The findings show that manufacturers are preferable to carbon abatement and capture optimal profits when shareholding is above a threshold under the retailer power equilibrium, but they may exert a worse negative impact on the environment. The dominant position of the held retailer is not always favorable to capturing the optimal SW and mitigating EIs. In addition, under the combined effect of competition level and shareholding, retailer power equilibrium scenarios are more favorable to improving SW and reducing EIs.

This paper inspects the combined influence of retailer competition and power structure on manufacturers' carbon abatement. Distinguishing from previous literature, the authors also consider the impact of vertical shareholding and consumer preferences. In addition, the authors analyze the SW and EIs in different scenarios.