An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou

Optimizing City-Scale Demolition Waste Supply Chain Under Different Carbon Policies

In order to establish a green, low-carbon circular development economic system, imperative goals include achieving carbon peaking and carbon neutrality. This research delves into the resource utilization of city-scale demolition waste (C&DW), aligning with environmental protection needs and sustainable development principles. The paper introduces a unique closed-loop supply chain (CLSC) model tailored for C&DW and employs a distinctive mixed integer nonlinear programming (MINLP) model for optimization. Guangzhou serves as a case study for thorough analysis, verification, and practical application of the proposed model, especially under diverse scenarios of carbon price (CP) and carbon trading (CT) policies. The key conclusions drawn from this study include the following: (1) The cost of carbon emissions is intricately influenced by both carbon emissions and carbon price, with the latter effectively regulating the carbon emissions during C&DW recycling. (2) The implementation of a CT policy, with a fixed carbon price, contributes to a further reduction in the cost of C&DW recycling treatment. (3) Under equivalent conditions, the CT policy demonstrates the potential to decrease costs and enhance the economic benefits within the building environmental protection product market. The research outcomes not only contribute to the advancement of management theory in the C&DW recycling supply chain (SC) but also provide a robust theoretical foundation for governmental initiatives aimed at introducing effective C&DW recycling management policies.

Decoupling economic growth from construction waste generation: Comparative analysis between the EU and China

The reduction and management of construction waste is crucial for the sustainable development of the construction industry. This research aims to explore a comparative analysis on decoupling relationship between economic growth and construction waste generation on European Union (EU) and Chi et al., 2020 to 2020 in the construction industry, through an integrated method framework of "Tapio + Kaya + LMDI". The research results indicate that there are significant differences in construction waste generation among different countries. The growth rates of construction waste in the EU and China from 2004 to 2020 were 2.47 % and 10.5 %, respectively, showing an upward trend. The economic growth of the construction industry in most EU countries is in a decoupling and negative decoupling state with significant regional differences in decoupling status. The construction waste generation in China is mainly in a weak decoupling state. Economic and demographic factors are the main factors promoting the increase in construction waste generation, while technological factors are the main factors inhibiting construction waste generation in EU and China. However, the impact of each factor on construction was generation varies from EU countries. The research reveals the decoupling effect mechanism between construction waste generation and economic growth, and improves the theory of construction waste management, promotes sustainable development. These findings have feasible inspiration for construction waste management in developing countries with different economic growth levels.

A comprehensive model for quantifying construction waste in high-rise buildings in India

The construction industry plays a vital role in the economic development of any country. Concurrently, the sector also generates enormous quantities of construction and demolition waste (CDW) that damages the ecology causing environmental pollution and deteriorating human health. Recently, various governments and other organizations realized the importance of implementing construction waste management (CWM) practices to attain sustainability in construction. The current decade can be called a pathway for achieving the 2030 agenda for sustainable development goals in which CWM plays an inevitable role. However, accurately quantifying construction waste is necessary to successfully implement any CDW management plan. A detailed literature review for the current research revealed that limited information on the magnitude of construction waste is available in India. Therefore, the current paper proposes a practically viable model to estimate the waste generation index (construction waste generated per total floor area) of high-rise residential buildings in India. The waste quantification is being done based on the project documents and expert interviews. The methodology is later validated through a high-rise building with G + 18 stories located in Kerala, India. The study indicated that a high-rise concrete framed structure generates 122.3 kg m-2 of waste during construction. It was also noted that, concrete, aggregates and blocks constitute 92% of the total waste generated in the project. The developed model can also be used as a cornerstone for establishing a construction waste database at the regional level.

Spatial differentiation of carbon emissions reduction potential for construction and demolition waste recycling

Identifying the regional differences and drivers for carbon reduction of construction and demolition waste (C&DW) recycling is essential to combat climate change. This study aims to calculate the carbon reduction potential for C&DW recycling from 2006 to 2021 in China and investigates the spatial differences and driving factors of carbon reduction potential for C&DW waste by combining the Theil index, Gini coefficient, and geographic detector methods. The carbon reduction potential for C&DW recycling in China was "high in the east and low in the west" overall level, with an average annual growth rate of 6.27%. The overall differences in carbon reduction potential for C&DW recycling are decreasing, mainly due to intraregional differences and inter-provincial differences in Northeast China. The population size, urbanization rate, and technological effect are the key factors influencing carbon reduction potential for C&DW recycling. There are two types of interactions between influencing factor pairs: nonlinear enhancement and two-factor enhancement. This study's results can guide policymakers to devise relevant, regionally specific policies.

Dynamic simulation of green technology innovation in large construction companies

The construction sector plays an important role in environmental sustainable development and the green economy. Green technology innovation in the construction sector can improve the energy, cost, and environmental performance of the industry. The lagging effects of influential factors for green technology innovation have yet to be fully understood. This study aims to explore the process of green technology innovation in large construction companies based on the innovation value chain theory and through a system dynamics (SD) approach. The results revealed the dynamic interaction between various influencing factors of green technology innovation in the construction industry. The effects of different knowledge bases and market shares show heterogeneity when the influencing factors are considered as an integrated system. The study helps researchers and practitioners gain a better understanding of the nature of green technology innovation from a systematic view. Suggestions are provided for decision-makers and practitioners to better manage green technology innovation.

Economic benefit analysis of the carbon potential of construction waste resource management based on a simulation of carbon trading policy

The need for safer and cleaner environments for all humankind remains a topical issue that cannot be overemphasized. To provide an updated perspective, this study analyzes the carbon potential of construction waste resource management based on carbon trading policy. In this study, the system dynamics principle was used to establish a carbon potential model of construction and demolition waste (C&DW) resource treatment by taking the regeneration project of Xiancun Village as an example. The results showed that the use of construction waste for recycling and resource treatment can generate enormous opportunities to reduce carbon emission. The implementation of the carbon trading policy can create significant benefits in terms of reducing carbon emission, while the total reduction of carbon emission in the baseline scenario can reach 100.66% when compared to the scenario without a carbon trading policy. Moreover, the findings shows that the combination of the carbon trading policy of "carbon price + free allowance ratio" can improve the return on investment of resource utilization companies and the carbon reduction benefits of the combined policy are greater than those of the single policy, but only if the level of the carbon price or free allowance ratio in the combined policy is accepted by the carbon trading subjects. The results of this research contribute to the theory of construction waste resourceization management, provide the theoretical basis for government departments to introduce carbon reduction policies for construction waste resourceization, and provide guidance for the management of companies' carbon reduction.

Evaluating the effects of policies on building construction waste management: a hybrid dynamic approach

The construction industry, as a vital pillar of a country's economy, generates a significant amount of construction waste, which places a tremendous burden on the environment and society. Although previous studies have explored the impact of policies on construction waste management, there is a lack of a simulation model that can be easily used, taking into account the dynamic nature, generality, and practicability of the model. To fill this gap, a hybrid dynamics model of construction waste management system is developed using agent-based modeling, system dynamics, perceived value, and experienced weighted attraction. Based on relevant data from the construction waste industry in Shenzhen, China, the effect of five policies on contractor strategy selection and overall evolution is tested. The results indicate that industry rectification policy and combination policy can effectively promote the resource treatment of construction waste and reduce illegal dumping, pollution to the environment of waste and treatment process, and waste treatment cost. The findings of this research will help not only researchers better analyze the effect of construction waste policies but also policymakers and practitioners in proposing effective construction waste management policies.

Study on characteristics and microscopic mechanism of composite environment-friendly dust suppressant for urban construction site soil fugitive dust based on response surface methodology optimization

Soil fugitive dust pollution caused by urban construction sites is a significant problem. To improve the dust suppression efficiency on the urban construction sites, hydroxypropyl guar (HPG), dodecyl dimethyl amine oxide (OB-2), and hydroxypropyl methylcellulose (HPMC) were selected as individual components of the composite dust suppressant using a single-factor test. The response surface methodology (RSM) was used to determine the optimal mixing proportions. After preparation, the characteristics of the composite dust suppressant were tested. Fourier-transform infrared spectroscopy and scanning electron microscopy (SEM) were used to characterize the composite dust suppressant and explore its mechanism. The results showed that 0.327% HPG, 0.6% OB-2, and 0.5% HPMC were the best compound concentrations. Under optimum conditions, the viscosity of the composite dust suppressant was 151.1 [Formula: see text], penetration time was 61.4 s, and water retention rate was 30.67%. Compared with traditional dust control by spraying water, it showed better resistance to evaporation at high temperatures and better wind erosion resistance. The antievaporation rate was 39.42% at 60 °C. After 11 d of continuous wind erosion at level 7, the wind erosion resistance rate was as high as 98.24%. The reason for the excellent dust suppression effect of the composite dust suppressant is that the methyl and hydroxyl groups in the solution diffuse to the surface of the soil fugitive dust particles using Brownian motion and gradually approach the corresponding groups in the soil fugitive dust particles. When the distance between the two reaches 10 [Formula: see text], adsorption occurs, causing small dust particles to stick together. Because of the stability of the covalent bonds in the methyl and hydroxyl groups, a stable solidified layer is formed on the soil fugitive dust surface after the evaporation of the composite dust inhibitor solution, thereby avoiding secondary dust. In addition, the composite dust suppressant is noncorrosive and friendly to the construction site environment. Therefore, the composite dust suppressant can effectively reduce soil fugitive dust, alleviate environmental pollution, and provide a reference for preventing and controlling soil fugitive dust on urban construction sites and preparing composite environment-friendly dust suppressants.

Exploring the nexus between energy consumption, income inequality and poverty, economic growth, and carbon dioxide emission: evidence from two step system generalized method of moments

The concern of environmental degradation, poverty, and income inequality remains a priority in achieving sustainable development goals. Countries are trying to reduce income inequality, alleviate poverty, and reduce environmental degradation which needs special attention. Consequently, this study explores the effect of income inequality, poverty, and energy consumption on carbon dioxide emission in the Belt and Road Initiative countries from 1996 to 2018. By employing the generalized method of moments, the findings show that income inequality, poverty, and energy consumption significantly increase carbon dioxide emission and lead to environmental degradation, while access to electricity significantly raises environmental quality. Economic growth positively affects carbon dioxide emission; however, the environmental Kuznets curve is valid. Income inequality exerts a moderating effect on carbon dioxide emission via per capita economic growth that reduces environmental degradation in the Belt and Road Initiative countries. The results of this study give important policy implications for the Belt and Road Initiative countries.

The nexus between natural resources, renewable energy consumption, economic growth, and carbon dioxide emission in BRI countries

This study investigates the nexus between natural resources, renewable energy consumption, economic growth, and carbon emission in 35 belt and road initiative (BRI) countries from 1985 to 2019. By employing OLS, fixed effect, generalized method of moments, and seemingly unrelated regression models, the results show that carbon dioxide and renewable energy are the driver factors of economic growth while natural resources reduce economic growth. The effect of economic growth and natural resources on carbon dioxide is positive; however, renewable energy consumption significantly reduces carbon emission. Economic growth rise renewable energy consumption while carbon dioxide and natural resources reduce it. The findings of this study have considerable policy implications for the belt and road countries that how natural resources and income inequality influence the interlinkage of renewable energy consumption, economic growth, and carbon dioxide emission.

Estimating the Carbon Emission of Construction Waste Recycling Using Grey Model and Life Cycle Assessment: A Case Study of Shanghai

Great efforts have been exerted in reducing carbon emissions in design, construction and operation stages. However, little attention is paid to the quantification of carbon emissions in construction waste recycling at the end-of-life stage. This study aims to quantitatively analyze the carbon emission of construction waste in Shanghai City, PR China. A grey model is used to forecast the generation amount of construction waste, and a life cycle assessment is performed to estimate the carbon emission of construction waste. In this study, both the carbon emission of recycling activities (environmental costs), and the equivalent amount of carbon generated from alternative materials (environmental benefit) are considered. Here, recycling 1 ton (t) of construction waste in Shanghai can save 100.4 kg CO2−e. The total carbon-emission-saving potential can be increased from 0.31 million t CO2−e (2022) to 0.35 million t CO2−e (2031). The carbon emission of recycling concrete, brick, steel, wood and mortar, identified as the key components of construction waste, is investigated. This research can help to reduce carbon emissions and further achieve carbon neutrality for Shanghai City. The proposed methods can also be applied to other regions, especially when the data for construction waste are insufficient.

Rural Planning Evaluation Based on Artificial Neural Network

The continuation of human civilization is inseparable from the development and construction of rural areas, and infrastructure is the core of rural development. China has been building large-scale rural infrastructure in recent years. Rural infrastructure building, for example, is huge in both quantity and scope, but it is beset by challenges in its current construction and development, and it urgently requires suitable leadership. Planning assessment, as a technical method, can identify problems in regional development and is a powerful tool for evaluating the impact of planning and construction and promoting the development of complete new areas. This paper is aimed at the planning evaluation of rural construction and the evaluation of rural construction and guides the planning and implementation of the next step of rural construction, to assist China's supervision and inspection of rural construction effect and promote rural construction and development into a good track. In view of the low accuracy and efficiency of the current evaluation model of rural planning and the problem that a single neural network easily produces local extreme value, the neural network method is improved, and the application of LM-BP neural network in the evaluation model of rural planning is proposed. Input sample elements are five factors affecting rural construction, including industrial construction, population distribution, and utilization rate of large-scale facilities, construction of public facilities, and promotion effect of supporting policies. Output sample is the evaluation result. On this foundation, the LM-BP neural network was used to convert the training into a least square problem, and the LM method was used to redefine the number of hidden layer nodes, resulting in the construction of a rural planning evaluation model based on the LM-BP neural network. This approach is used to determine the outcomes of rural planning evaluations. The experimental results show that the designed evaluation model has a small evaluation error, has the advantage of high accuracy compared with similar models, and is a reliable evaluation model for rural planning.

Construction and Demolition Waste Management Research: A Science Mapping Analysis

Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.

Method of Construction Projects’ Classification for Habitat Assessment in Poland and the Problem of Choosing Materials Solutions

The planning stage plays a key role in the success of each construction project. It also pertains to projects implementedin the Natura 2000 areas that cover ca. 18% of the total land area in the EU. Permission for the realization of such a project is issued after an analysis of its environmental impact on the Natura 2000 area. An important part of the analysis undertaken as part of a habitat assessment should be the evaluation of proposed material solutions. The research has revealed that habitat assessments in Poland do not fulfill this postulation. The decision-making process is based on the legal qualification criteria, and the fundamental importance in it has a precautionary principle. Practical realization of this principle demonstrates, however, shortcomings in its methodology. The article presents the results of two research stages. In the first stage, the documentation of 292 construction projects was examined in order to prepare the principal components of a checklist. They are correlated to the legal qualification criteria. However, they are more precise and systematic. In the second stage of the research, a survey of 47 experts was performed, and the result of the research is an innovative module of the checklist for qualification of construction projects to the habitat assessment, including questions on materials solutions. The research has proved that introduction of this proposal to the checklist may improve the quality of habitat assessments, increase their trustworthiness and ensure full exploitation of the possibilities which are given by the use of uniform research methods.

Evaluating an economic application of renewable generated hydrogen: A way forward for green economic performance and policy measures

Energy security and environmental measurements are incomplete without renewable energy; therefore, there is a dire need to explore new energy sources. Hence, this study aimed to measure the wind power potential to generate renewable hydrogen (H2), including its production and supply cost. This study used first-order engineering model and net present value to measure the levelized cost of wind-generated renewable hydrogen by using the data source of the Pakistan Meteorological Department and State Bank of Pakistan. Results showed that the use of surplus wind and renewable hydrogen energy for green economic production is suggested as an innovative project option for large-scale hydrogen use. The key annual running expenses for hydrogen are electricity and storage costs, which have a significant impact on the costs of renewable hydrogen. The results also indicated that the project can potentially cut carbon dioxide (CO2) pollution by 139 million metric tons and raise revenue for wind power plants by US$2998.52 million. The renewable electrolyzer plants avoided CO2 at a rate of US$24.9-36.9/ton under baseload service, relative to US$44.3/ton for the benchmark. However, in the more practical mid-load situation, these plants have significant benefits. Further, the wind-generated renewable hydrogen delivers 6-11% larger annual rate of return than the standard CO2 catch plant due to their capacity to remain running and supply hydrogen to the consumer through periods of plentiful wind and heat. Also, the measured levelized output cost of hydrogen (LCOH) was US$6.22/kgH2, and for the PEC system, it was US$8.43/kgH2. Finally, it is a mutually agreed consensus among environmental scientists that the integration of renewable energy is the way forward to increase energy security and environmental performance by ensuring uninterrupted clean and green energy. This application has the potential to address Pakistan's urgent issues of large-scale surplus wind- and solar-generated energy, as well as rising energy demand.

Caring for the environment: measuring the dynamic impact of remittances and FDI on CO2 emissions in China

Various old nexuses are getting new empirical attention in advanced econometric. Therefore, we examine the asymmetric influence of remittances and FDI on CO2 emissions by using the NARDL approach for China from 1981 to 2019. Based on NARDL empirical findings, a negative change in remittances has also positive effects on CO2 emissions in the short and long run. We found that positive and negative change in FDI has also a positive effect on CO2 emissions, while a positive change in FDI is relatively more effective on CO2 emissions than a negative change in FDI in long run. Asymmetry is observed in the only magnitude but not in direction. Our study implies that the China government should redesign the environmentally friendly policies and enforces the foreign investors to role play in environmental quality.

How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries

This study measures the environmental regulation effect and pattern of carbon emission and energy efficiency through data envelopment analysis and econometric estimation. One of the most important ways to achieve a green transition is promoting technical progress through environmental regulation. Though China has witnessed rapid economic growth over the last two decades, the country can improve it further through adopting sustainable green energy and establishing more energy-efficient industries to strike a good balance between economic and social developments. The oil and carbon dioxide emission performances form the most important metrics. This study uses panel data from 30 Chinese provinces from 2008 to 2017 to assess the effect of environmental regulation on energy production. The nonradial directional distance function (NDDF) is used to measure the total factor energy efficiency index (TFEEI). The panel system GMM model, which can effectively address endogenous problems and regional variability, is utilized to research the nonlinear relationship between environmental regulations and EEI under various environmental regulations to study it. The findings reveal a considerably modest total average EEI amount for energy-intensive industries, averaging between 0.55 and 0.58, which is way below the ideal value (i.e., 1). Furthermore, the results of the dynamic panel data model revealed a significant U-shaped relationship between China's EEI and environmental regulation. The results show that as the values of market-based environmental regulations (MERs) and command and control environmental regulations (CCERs) exceed the corresponding levels, the impact of environmental regulation on the TFEEI increases gradually. This study will aid policymakers in better understanding the efficacy of different levels of environmental regulations to make more educated decisions.

Energy policy paradox on environmental performance: The moderating role of renewable energy patents

The environmental and socioeconomic considerations of energy production have become crucial because of the increasingly complex relationship between energy and the environment. This study aims to develop possible mechanisms for perspectives on energy policy and the environment by exploring the mediating role of renewable energy patents. Non-radial data envelopment analysis and panel data models are applied using the panel data from 2010 to 2017 from 30 Chinese provinces. The results show an overall improvement in the environmental performance index (EPI) of China's provinces, but the average EPI is still relatively weak, with an average value between 0.44 and 0.52, which is far below the optimal value 1.. Furthermore, the econometric model offers evidence that provincial renewable energy and emission reduction policies positively impact the enhancement of EPI. The findings have several implications for energy and environmental policies.

Nexus between energy policy and environmental performance in China: The moderating role of green finance adopted firms

This study measures the association between resources and the atmosphere; social and environmental aspects of energy production have become critical. In this context, the aim of this research is to explore the mediating effect of renewable energy patents in developing potential frameworks for energy policy viewpoints on the climate. The study took panel data from 2010 to 2017 and used a non-radial data envelopment analysis (DEA) process and panel data model for 30 Chinese provinces. The findings indicate that between 2010 and 2017, the average environmental efficiency index (EPI) of Chinese areas increased by 9.88%. When firms' internal variables are proxied by their commodity (revenue), the relationship term's point approximate coefficient is about 0.05. This magnitude means that a 1% rise in a company's assets will result in a 5% increase is estimated to be about 0.157, implying that a 1% rise in firm leverage is correlated with a 15.7%. Finally, based on the study results, some policy implications were proposed.

Determinants and implications of environmental practices for waste management and the minimization in the construction industry: a case study of Pakistan

The construction projects and activities generate waste materials, which impose negative impacts on the environment and contribute towards environmental degradation. In this regard, the implementation of environmental practices (EPs) can play a vital role in reducing the environmental risks associated with waste materials from construction projects. Based on this hypothesis, the present survey study was conducted to assess the effectiveness of different EPs in reducing environmental risks associated with waste materials from construction firms (n = 159) operating in Pakistan. Organizational and government support, regulatory pressure, and economic and environmental performance were among the main determinants of EPs studied in the present study. The partial least squares technique was used for the data collection, assessment, and prediction of the results based on the hypothesis testing for a range of determinants. Compose reliability analysis of determinants showed that all items gave a value of 0.7, which is a clear indication of the reliability of each determinant in the formation of the hypothesis. From all eight hypotheses, H₁ (0.475), H₄ (0.217), H₆ (0.114), H₇ (0.210), and H₈ (0.149) hypotheses with size effect in parentheses were acceptable due to their positive construction with EPs, while H₂, H₃, and H₅ hypothesis did not show the significant effect with size effect values lower than 0.1. The study demonstrated that current environmental regulations and governing bodies in Pakistan are not sufficiently effective and strict to implement environmental regulations. In this regard, regulatory pressure is necessary to promote EPs along with increasing stakeholders' awareness. Overall, the implementation of EPs not only prepares construction firms to deal with the pressure exerted by regulations and customers but also enhances the environmental and economic performance of construction firms.

Stock markets dynamics and environmental pollution: emerging issues and policy options in Asia

The fact is the stock market has an asymmetric effect on macroeconomic variables. In this study, we examine the nonlinear stock market reaction to the environment. This is the first study that considers the possibility of asymmetric effects of stock market on environmental pollution in Asia. This study considers the experiences of Asia economies by using the panel NARDL methodology over the data period from 1995 to 2019. The long-run panel NARDL results showed that the positive change in stock market increases carbon emissions. In adverse, the negative change in stock market significantly mitigates the carbon emissions in Asia. The short-run stock market asymmetric effects continued into the long-run asymmetric effects on the environment in Asia. Thus, policymakers and authorities should initiate to promote green financial activities in Asian stock markets.

How energy insecurity leads to energy poverty? Do environmental consideration and climate change concerns matters

The aim of the study is to estimate the nexus between energy insecurity and energy poverty with the role of climate change and other environmental concerns. We used DEA like WP methods and properties of MCDA, a most common form of data envelopment analysis (DEA) to estimate the nexus between constructs. This paper presents a measurement and analysis of G7 countries' energy, economic, social, and environmental performance associated with energy poverty indexes. The study used the multiple, comprehensive, and relevant set of indicators, including energy economics and environmental consideration of energy poverty. The net energy consumption of al G7 economies is equal to 34 percent of the entire world along with the net estimate GDP score of around 50 percent. Using DEA modelling and estimation technique, our research presented valuable insights for readers, theorists and policy makers on energy, environment, energy poverty and climate change mitigation. For this reasons, all these indicators combined in a mathematical composite indicator to measure energy, economic, social, and environmental performance index (EPI). Results show that Canada has the highest EPII score, which shows that Canada's capacity to deal with energy self-sufficiency, economic development, and environmental performance is greater than the other G7 countries. France and Italy rank second and third. Japan comes next with 0.50 EPI scores, while the USA has the lowest average EPI score environment vulnerable even though have higher economic development among the G7 group countries. We suggest a policy framework to strengthen the subject matter of the study.

Evaluating green innovation and performance of financial development: mediating concerns of environmental regulation

This research measures the relationship between green innovation and the performance of financial development by using an econometric estimation during the year of 2000 to 2018 in 28 Chinese provinces. It is intended to explore the relative role of green technological innovation in driving green financial development in the west and central China, as well as how it influences economic growth in these regions. Ordinary least square (OLS) framework was utilized in mainland China to perform empirical studies by using an econometric estimation. This study claims that China has adopted research-based education system, while those for economic growth and expenditure in the regions while the innovation parts results shows that the  tertiary education were 12.42% and 13.53% versus the 10.50% and 10.6% in the eastern area. The research-based education increases the patents in green innovation and boosts the environmental policy. The financial development led to green technological development and innovation. Green innovation and financial development decrease the emissions, and it is apparent that as environmental regulations stimulate technical development, the superiority of human resources increases. The findings indicate that green financing reduces short-term lending, thus limiting clean energy overinvestment, while the long-term loans have little impact on renewable energy overinvestment, and the intermediary effect is unmaintainable. Meanwhile, the green financial growth will reduce renewable energy overinvestment and increase renewable energy investment productivity to certain amount.

Analysis of income inequality and environmental pollution in BRICS using fresh asymmetric approach

With rapid economic growth, BRICS is facing enormous burdens of carbon emission and severe issues of income inequality. However, behind this economic success, the BRICS economies also face few thoughtful challenges to improve environmental quality by catching up the sustainable development goals. Consequently, the existing empirical research is concerned with the dynamic links between income inequality and CO₂ emissions by using the novel nonlinear ARDL approach, but small attention has been paid to the BRICS in literature. Therefore, we observed that a negative and positive change in income inequality has positive effect on CO₂ emissions in Russia and South Africa in the long run, although a positive change in income inequality has positive effects on CO₂ emissions in Brazil, Russia, and China, while a negative change in income inequality has negative effect on CO₂ emissions in India, Brazil, and Russia in the short run. Hence, the findings value specific attention from policymakers in BRICS economies.

Toward a sustainable environment: nexus between consumption-based carbon emissions, economic growth, renewable energy and technological innovation in Brazil

This research investigates the drivers of consumption-based carbon emissions in Brazil by using a dataset covering the period between 1990 and 2018. These dynamics were examined by employing the ARDL bounds, DOLS, and gradual shift causality tests. The ARDL long- and short-run estimation outcomes reveal that: (a) renewable energy use stimulates the sustainability of the environment; (b) economic growth increases environmental degradation; and (c) technological innovation enhances the quality of the environment. In addition, the gradual shift causality test results disclosed that renewable energy consumption, economic growth, technological innovation and public-private partnership investment in energy can predict consumption-based carbon emissions in Brazil. Therefore, Brazilian policymakers should actively encourage the R&D of low-carbon technologies and renewable energy consumption. Domestic consumption levels, on the other hand, should be targeted, specifically those that are more energy-intensive and cause a rise in CO₂ emissions due to consumption.

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

Analysis of the Scientific Evolution of the Circular Economy Applied to Construction and Demolition Waste

The circular economy (CE) concept applied to construction and demolition waste (CDW) is a developing field of research that entails a large amount of complex and disjointed information; thus, a comprehensive review of scientific contributions could provide a completed perspective. This article aims to analyse the existing knowledge of CE research applied to CDW, using a double integrated analysis, a systematic literature review and a bibliometric analysis. For this purpose, Science Mapping Analysis Tool (SciMAT), a software for the analysis of performance indicators and visualisations of scientific maps, has been used, which offers a complete approach to the field and evaluates the most cited and productive authors and subject areas related to this discipline. The results obtained from the 1440 bibliographic records from 1993 to 2020 show a still-developing scientific field, evolving from concerns about economic aspects to the most recent progresses in the evaluation of sustainable deconstruction. This work will contribute to the existing body of knowledge by establishing connections, mapping networks of researchers and recommending new trends.

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.

Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

System dynamics approaches to assess the impacts of climate change on surface water quality and quantity: case study of Karoun River, Iran

The aim of this research is to gain a better understanding of the effects of climate change with a comprehensive and dynamic perspective. Therefore, by using the System Dynamics (SD) approach to simulate the effects of climate change on the quality and quantity of the Karoun River and regarding the water supply and demand systems in the region and their feedback relations, a model was developed in Vensim. CGCM3 outputs under A2, B1, and A1B emission scenarios have been used to investigate the effects of climate change on both the quality/quantity of the water resources system. Also, to determine the effects of climate change on agricultural demand, the water requirement of selected crops for the next period (2015-2050) has been calculated via CROPWAT model. The results show that the maximum and minimum temperature and evaporation will increase. The results of the developed SD model show that if the current development process continues under all three climate change scenarios, the system will be able to meet the domestic, industrial, and environmental demand. However, the supply of agricultural demand will be deficient. Also, the average EC value in Ahvaz station under three emission scenarios has increased more than 21%, compared to the 15-year average. The average pH value did not change much. Then, several proposed management scenarios were evaluated to improve system performance. The results show that the scenario of optimal operation of upstream dams has the best performance. However, due to the unrealistic growing trend, despite applying this scenario, the development of the agricultural sector will fail down after a few years. Therefore, to reach a long-term solution to the problem of water shortage, the growth trend of this sector for the next period should be reviewed in light of the effects of climate change.

Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

GHG Emissions Assessment of Civil Construction Waste Disposal and Transportation Process in the Eastern Amazon

The urbanization process represented by an increased supply of housing and transport infrastructure has taken place at an accelerated rate in several regions of Brazil, especially in the metropolitan areas of the Brazilian Amazon. Despite the existence of environmental policies that guide the proper disposal of civil construction waste (CCW) in Brazil, the impacts of these policies are still negligible, pointing to the need to establish other metrics such as the measurement of greenhouse gas (GHG) emissions in CO2eq associated with civil construction waste. This work aims to evaluate, in the second-largest city in the Brazilian Amazon, the environmental impact generated by the transportation of CCW to disposal sites, having as indicators the volume of this waste and the CO2 emissions produced during a whole year. A literature review on life cycle carbon emissions assessment in building construction and CO2 emissions in transportation are provided to establish the background of the research methodology. Data collection was carried out by searching large generators of construction waste, the companies responsible for transporting construction waste, and the types of vehicles used. Calculation of GHG emissions from CCW transportation was based on the method described in the 2006 IPCC Guidelines. The study identified a volume of waste of around 1244 m3/month, with a generation of 40,440 kgCO2/year, only from small and large generators. Besides the damage identified in this study, there is also the dumping of CCW into urban streams in the city which is causing negative impacts on sanitation and drainage systems. The results point to the need to strengthen local policies to mitigate the impacts of the existing CCW to contribute to a more sustainable city.

Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

Research on Freight Transportation Carbon Emission Reduction Based on System Dynamics

In order to solve the environmental protection problem of carbon emissions in the field of freight transportation, this article proposes to promote the transfer of road freight transportation to railway transportation within a reasonable range by levying carbon emission taxes. To propose an applicable solution, this paper establishes a comprehensive carbon emission system model in the field of road transportation and railway transportation to simulate a closed-loop system as comprehensively as a real transportation system, determines the system elements according to the actual situation, reasonably develops the model hypothesis scheme, and draws out the causal network. On this basis, the system flow diagram and corresponding structural equations are constructed, and the model parameters are estimated. Finally, the paper uses actual data to verify and simulate the system model. A reasonable carbon levy interval has been obtained, and the carbon levy within this interval can promote the transfer of road freight transportation to railway transportation, so as to achieve the purpose of decreasing total carbon emissions of road–rail transportation systems in an orderly way. The innovation of this paper is to construct the carbon emissions of the road–rail system systematically for the first time, and to conduct research and exploration of carbon levies on this basis.

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.

Benign or disordered development? Assessment and simulation of security of highly aggregated tourist crowds in China

Arising with increasing security issues in highly aggregated tourist crowds (HATCs), widespread attention has been dedicated to security status. Assessing and forecasting the security status of HATCs in various situations related to tourist destinations is an important strategy of security management. Thus, this study constructed a system dynamic flow diagram for the security evaluation of HATCs. The relevant data were collected on perceptions of crowded tourists through questionnaires at Tianyou Peak during China's National Day (Golden Week Holiday). Additionally, efforts were made to conduct online surveys at Shanghai Disney Park and Shilin Night Market in Taipei, since crowding always occurs in these two areas. Empirical results based on Vensim software suggest that HATC status is the result of the coupling of various influencing factors and the result of the benign coupling of the three subsystems: multi-source pressure, state variation, and management response. HATC security presents a changing trend of “increase-decrease-recovery”. Differences exist in the changes of HATC security status in different spaces and at different time nodes. The findings also indicated that HATCs that appear in the daytime are more stable than HATCs that appear at special time nodes. This study highlighted that the security management of HATCs should focus on systematization, differentiation, and precision management.