Exploring the industrial solid wastes management system: Empirical analysis of forecasting and safeguard mechanisms

Get the win-win: Sustainable circular model of 'generation-value-technology' of industrial solid waste management

The management of industrial solid waste (ISW) and promoting sustainable circular development of the industrial economy is an urgent priority today. Therefore, this article constructs a sustainable circular model of 'generation-value-technology' of ISW management through the lens of industrial added value (IAV) and technology level. Also, the importance of the role of government is considered in the model. Based on actual data of China, this article simulates the future trend of the model using a system dynamics approach. The chief findings of the study are as follows: (1) under the current policy, China's future industrialization is increasing and the technological level of industrial enterprises is rising, but this is accompanied by a climb in ISW generation. (2) The win-win situation of ISW decrease and IAV increase can be achieved through enhanced information disclosure, technology innovation and government incentives. (3) Government subsidy should be oriented towards supporting technology innovation in industrial enterprises while reducing the proportion of incentives for ISW management results. Based on the results, this study proposes targeted policy implications for government and industrial enterprises.

Development of an industrial solid waste ecological analysis model in Shanghai, China

Amid China's rapid economic expansion, the country's industrial solid waste (ISW) problem is escalating. As each sector generates distinct types of ISW, a multi-indicator assessment of each sector is essential to address China's New Solid Waste Policy. To investigate the ISW situation of each sector and perform a comprehensive assessment, we formulate an industrial solid waste ecological analysis framework based on ISW generation and ISW flow in the sector. Various indicators (i.e., solid waste utilization coefficient, solid waste threat coefficient, and solid waste threat intensity) are employed to assess the utilization of solid waste generated for each sector, as well as the threat of solid waste originating in each sector to society. Ecological network analysis probes the interrelationships between diverse sectors. Taking Shanghai in 2017 as an example, the study indicates that some sectors (e.g., production and supply of electric power and heat power (EH) and metal smelting and rolling processing sector (MS)) exhibit higher direct ISW generation and the direct industrial solid waste value-added coefficient (SVAC) for common industrial solid waste (CISW). Specifically, the direct CISW generation of EH and MS is 539.21Mt and 277.00Mt respectively. The direct SVAC of EH and MS is 157.06kg/103RMB and 126.27kg/103RMB respectively. These sectors should prioritize reducing emissions at the source. Additionally, the threats to society from various sectors are relatively insignificant for the CISW, while for the hazardous waste (HW), all sectors pose a considerable threat to Shanghai's society. Moreover, some sectors (e.g., mining industry) exhibit the highest mutualism relationships in the CISW and the HW. Enhancing mining sector technologies is a vital strategy for mitigating ISW sources. Specifically, MI has 9 pairs of mutualism relationships in the CISW and 8 pairs in the HW. These insights will provide empirical evidence for tackling the ISW problem in Shanghai.

Environmental and safety risk assessment for sustainable circular production: Case study in plastic processing for fashion products

Even if sustainability and circularity are the most challenging goals today, industrial waste minimization is rarely discussed and practical methods to reduce risks related to hazardous waste in manufacturing processes are not frequently applied yet. The case of Italian company specialized in plastic processing for fashion products, has the chance to design and test a new integrated methodology to reduce the risks for ecosystem and workers associated to hazardous waste. Focusing the attention to standard operations, extraordinary conditions, and emergency situations, all activities included in waste collection, storage and transport are identified and the risks associated to the environmental impacts and the occupational health&safety are analysed. Research results demonstrate the opportunity to adopt one method to analyse both environmental and health&safety risks associated to activities and conditions involved in industrial waste management. The case study confirms the relevance of integrated approaches and the necessity of simplified tools to support companies in adopting integrated risk management.

Hierarchy performance assessment of industrial solid waste utilization - tracking resource recycling and utilization centers in China

The massive production and accumulation of industrial solid waste (ISW) have led to environmental pollution and natural resource underutilization. China's efforts to build trial industrial waste resource utilization centers provide strong support for sustainable development. However, these centers and the factors driving ISW utilization have yet to be evaluated. This paper utilizes context-dependent data envelopment analysis models without explicit inputs (DEA-WEI) to evaluate the overall utilization performance of 48 industrial waste resource utilization centers in China from 2018 to 2020. It also builds a Tobit model to assess which indicators and waste types affect overall ISW utilization. The results show overall ISW utilization performance of centers in the sample has improved, with the average value falling from 1.7193 in 2018 to 1.5624 in 2020. However, there are clear regional performance gaps, with East China having the highest utilization performance (1.3113) while the Southwest had the lowest (2.2958). Finally, this paper proposes measures to improve the overall utilization of industrial waste resources based on an analysis of the factors driving solid waste utilization.

Can industrial symbiosis policies be effective? Evidence from the nationwide industrial symbiosis system in China

Policies directly or indirectly influence the development of industrial symbiosis (IS). Quantitatively analyzing the effects of policies on IS at a national level is necessary, but current research has lagged. Focusing on the symbiotic system that includes the thermal power industry, cement industry, iron and steel industry, and social sector in China, this paper assesses the efficacy of policies on this nationwide IS system between 2015 and 2022. A policy influence framework is proposed, combining a cost-benefit analysis, agent-based model, and comparative analysis. Results show: (1) the symbiosis probability of the nationwide IS system experiences a fluctuating increase. The maximum increments of the symbiosis probability are 5%, and the resulting environmental benefits are equivalent to an emission reduction of 6.99 Mt from blast furnace slag, 20.97 Mt from iron mine tailing, 36.02 Mt from household waste, 25.01 Mt from steel slag, and 22.95 Mt from fly ash. However, the stimulation effects of policies vary across different subsystems. (2) Thermal power-chemical subsystems, thermal power-environmental protection subsystems, iron and steel-environmental protection subsystems, and social sector-cement subsystems need policy support in the future. (3) Approximately 50% of fields in this nationwide IS system is insensitive to current policies; policy approaches should shift from economic stimulation to symbiotic guidance. This paper fills the research gap by quantitatively studying the IS policy efficacy from a national level. The findings can contribute to the improvement of the Chinese IS policy system.

Decomposition, decoupling, and future trends of environmental effects in the Beijing-Tianjin-Hebei region: A regional heterogeneity-based analysis

The green growth of Beijing-Tianjin-Hebei (BTH) urban agglomeration plays a leading and exemplary role in overcoming internal resource restrictions, addressing climate change, and supporting China's high-quality growth. From the standpoint of pollution reduction and carbon reduction, this paper first conducts a comprehensive evaluation of the environmental impact based on combined weighting technique. The Logarithmic Mean Divisia Index (LMDI) model is used to decompose the environmental impact drivers in distinct areas. A decoupling effort index is further constructed to measure the effect of various efforts on the decoupling of economic growth and environmental impact, the improved grey Markov model is applied to predict the future trend of regional decoupling efforts. The results of empirical analysis based on data of the BTH region during 2011-2018 show that: 1) the environmental impact index of Beijing is the lowest followed by Tianjin and Hebei; 2) environmental regulation exerts the most significant impact on reducing environmental pressure in Beijing while technology progress and energy intensity have the most significant effect on easing environmental pressure in Tianjin; 3) strong decoupling efforts have been found in Beijing, Tianjin and Hebei, however, such effect is more significant in Beijing; 4) Beijing's decoupling state is mostly driven by regulatory effect, intensity effect, and scale effect, while Tianjin and Hebei's decoupling states are primarily driven by improvements in environmental regulation and energy intensity; 5) according to the forecast outcome of the improved grey Markov technique, a state of strong decoupling effort will be maintained in the BTH area by 2025, and the decoupling effort index in Beijing will remain the highest while the index in Hebei will remain the lowest.

Machine-learning approaches in geo-environmental engineering: Exploring smart solid waste management

Over the past few decades, increased attention has been paid to domestic waste (DW) generation. DW comprises a large percentage of municipal solid waste (MSW), and its handling and processing involves serious technical issues while also consuming a major portion of municipal budgets. The accurate estimation, prediction, and characterization of DW is an ongoing challenge for many cities, municipalities, and local governments as they strive to implement sustainable strategies for MSW. The main objective of the present study is to estimate and correctly predict DW quantities using machine-learning (ML) algorithms. Several different ML algorithms are used in the research, including linear regression, regression trees, Gaussian process regression, support vector machine, and autoregressive integrated moving average methods for time series analysis. Two case studies are presented in this paper. In the first, domestic waste data covering the period from 2010 to 2021 were collected from the Saudi and Bahrain authorities, and in the second, the domestic waste-generating behavior of a family of eleven members was followed for one month. The results show that the biodegradable and non-biodegradable wastes generated by the family were in the range of 1.7-7.9 kg and 0.0-2.0 kg, respectively, and promising outcomes were obtained using an appropriate selection of input predictors in conjunction with time series analysis. The trained models are validated and tested using several types of evaluation metrics, including calculated residuals, mean square error, root mean square error, and coefficient determination (R²-Score). The latter values are in the range of 0.67-0.85 for the training and testing datasets for many of the predicted waste quantities. The results obtained from the study show that these algorithms can be used to reduce the environmental, economic, and societal impacts of waste by designing a smart waste management engineering system.

Catalytic Pyrolysis of Waste Plastics over Industrial Organic Solid-Waste-Derived Activated Carbon: Impacts of Activation Agents

Renewable source-derived carbon is found to be a green alternative catalyst to zeolite for the pyrolysis of plastics. However, only polyethylene (PE) catalytic pyrolysis over biomass-derived carbon has been extensively studied. In this work, carbon was produced from industrial organic solid waste using different activation agents, and their catalytic performance on the thermal degradation of typical polymers, namely PE, polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) were investigated. The degradation mechanisms and the roles of different active sites of the carbons are discussed. Steam failed to activate the carbon, which has a low specific surface area (6.7 m2/g). Chemical activation using H3PO4 and ZnCl2 produces carbons with higher specific surface area and more porosity. The pyrolysis characteristics of LDPE, PP, PS, and PET catalyzed by the carbons were studied using TGA and a fixed-bed reactor. The thermogravimetric results indicate that all three carbons reduce the pyrolysis temperature. The analysis of the products shows that the P- and Zn-involved acid sites on the AC-HP and AC-ZN change the reaction pathway of plastics and promote: (1) C-C cracking and aromatization of polyolefins; (2) the protonation of phenyl carbon of PS to yield higher benzene, toluene, and ethylbenzene; and (3) the decarboxylation of the terephthalic acid intermediate of PET, resulting in higher CO2 and benzene. In addition, the high-value chemicals, long-chain alkylbenzenes, were found in the liquids of AC-ZN and AC-HP. The long-chain alkylbenzenes are probably formed by acid-catalyzed alkylation of aromatic hydrocarbons. This study provides basic data for the development of a cheap catalyst for plastic pyrolysis.

Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China

Improving industrial ecological efficiency is important in promoting the industry’s sustainable development. However, the economy, resources, the environment, and other factors should be considered. This paper proposes a data-driven evaluation and promotion method for improving industrial ecological efficiency. Based on industrial input and output data, the super-efficiency slack-based model containing an unexpected output was used to measure industrial ecological efficiency. The kernel density estimation method was employed to analyze the time-series characteristics of industrial ecological efficiency. Using data from 30 provinces and cities in China, this study demonstrated the implementation of a data-driven method. The results show that China’s overall industrial ecological efficiency is increasing, and industrial ecological efficiency in the western region is rapidly improving. Differences exist between provinces and cities; the characteristics of polarization are significant, and there are short boards in the eastern, central, and western regions. Based on this, suggestions are made to improve the industrial ecological efficiency of the central region, narrow the gaps between the regions, and promote each region to develop its strengths and mitigate its weaknesses. This provides a basis for formulating policies related to ecological environment protection and industrial pollution control.

Prediction of China’s Industrial Solid Waste Generation Based on the PCA-NARBP Model

Industrial solid waste (ISW) accounts for the most significant proportion of solid waste in China. Improper treatment of ISW will cause significant environmental pollution. As the basis of decision-making and the management of solid waste resource utilization, the accurate prediction of industrial solid waste generation (ISWG) is crucial. Therefore, combined with China’s national conditions, this paper selects 14 influential factors in four aspects: society, economy, environment and technology, and then proposes a new prediction model called the principal component analysis nonlinear autoregressive back propagation (PCA-NARBP) neural network model. Compared with the back propagation (BP) neural network model and nonlinear autoregressive back propagation (NARBP) neural network model, the mean absolute percentage error (MAPE) of this model reaches 1.25%, which shows that it is more accurate, includes fewer errors and is more generalizable. An example is given to verify the effectiveness, feasibility and stability of the model. The forecast results show that the output of ISW in China will still show an upward trend in the next decade, and limit the total amount to about 4.6 billion tons. This can not only provide data support for decision-makers, but also put forward targeted suggestions on the current management situation in China.