Analysis of driving factors on China's industrial solid waste generation: Insights from critical supply chains

What drives water conservation in the supply chain of the Yellow River Basin? An empirical analysis based on SPD

Industrial water saving is an objective requirement for the high-quality development of the Yellow River Basin, as water resource is the largest rigid constraint. In this study, water resources input-output model, structural decomposition analysis (SDA) and structural path analysis (SPA) were constructed to decompose the driving factors of total water use in typical water-deficient provinces (Ningxia, Shanxi, and Henan) in China's Yellow River Basin, to calculate their water use at each production stage and identify their key water-saving pathways. The results were as follows: (i) Water intensity had the most obvious impact on total water saving, resulting in efficiency improvements of 81.39%, 9.21%, and 78.45% for each province, respectively. The next factor was the final demand structure, which suppressed total water-saving efforts by 24.23%, 11.52%, and 113.12% in the respective provinces. (ii) The key water-saving paths in the typical water-deficient provinces of the Yellow River Basin were primarily centered around Sector 1. (iii) Water intensity had a strong water-saving effect on the key paths in the three provinces, with contribution rates of 100.42%, 59.02%, and 42.34% for Ningxia, Henan, and Shanxi, respectively. Final demand also contributed to water-saving in the key paths of Shanxi and Henan, with contribution rates of 35.06% and 28.23%, respectively. However, it inhibited water-saving efforts in the key paths of Ningxia, reducing it by 8.64%. Policy measures should be tailored to local conditions.

Analysis of the dynamics of common industrial solid waste based on input-output: A case study of Shanghai international metropolis in China

Rapid economic development inevitably leads to the discharge of a large amount of common industrial solid waste (CISW), which not only pollutes the environment but also restricts economic development in turn. In this study, a dynamic common industrial solid waste (DCISW) model is developed to analyze the retrospectively CISW development in Shanghai from 2000 to 2017 and simulate prospectively multi-perspective emission reduction measures. Specifically, the dynamic linkages between CISW and socio-economics are established at both the systematic and sectoral levels through input-output analysis, ecological network analysis, and related socio-economic indicators. Abatement measure simulations are conducted using RAS and other methods. The findings reveal that the growth of CISW in Shanghai is closely related to its fast-growing economy. The Others sector (OS) receives the most CISW from other sectors, while the Mining industry (MI) sends the most CISW to others. From production-based, consumption-based, and income-based perspectives, Chemical Products (CP) consistently remain the largest contributors. For the proposed waste generation intensity (WGI) indicator, the Mining industry (MI) is regarded as the main sector in all three perspectives. Moreover, the MI consistently emerges as the key sector of the system. CISW simulations indicate that improving the MI emission technology and increasing the MI output value will be top priorities for CISW emission reduction in Shanghai. This study is expected to provide scientific support to address the CISW problem in Shanghai and other similar fast-growing metropolises.

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.

Identifying the socioeconomic determinants of industrial hazardous waste generation: South Korea as a case study

The South Korean government has set an ambitious target to reduce industrial hazardous waste (IHW) as part of its transition towards a circular economy. Moreover, effective management of IHW within the country has become crucial, given that IHW trade is regulated by the Basel Convention. Despite the urgent need for well-founded environmental policies, there is a lack of essential information on the characteristics and determinants of IHW generation, which hinders the effectiveness of existing IHW policies. To address this information gap, this study developed a South Korean extended IHW input-output model for 2008 and 2018 to characterize IHW generation and applied structural decomposition analysis to identify the socioeconomic determinant of change of IHW generation. The results reveal that consumption, export, and direct IHW intensity change of 'Chemical', 'Electronic and electrical equipment', 'Basic metal', and 'Other service' emerge as dominant determinants for IHW growth. Conversely, technology change, including technological structure change and direct IHW intensity change, of 'Basic metal' and 'Other service' is the key driver for IHW reduction. In addition, an intriguing aspect of the study relates to the supply chain's influence on IHW generation. The indirect growth of IHW resulting from expanding exports and consumption contributes nearly twice as much to the overall increase in IHW as direct IHW growth. These valuable insights pave the way for the South Korean government to establish holistic and customized environmental policies regarding IHW. It emphasizes the importance of considering expanded global system boundaries, technological advancements, and purchasers' consumption patterns as dominant factors in formulating these policies. Furthermore, this study not only provides crucial guidance for the government's decision-making but also suggests strengthening environmental management and monitoring practices.

Large Virtual Transboundary Hazardous Waste Flows: The Case of China

The Basel Convention and prior studies mainly focused on the physical transboundary movements of hazardous waste (transporting waste from one region to another for cheaper disposal). Here, we take China, the world's largest waste producer, as an example and reveal the virtual hazardous waste flows in trade (outsourcing waste by importing waste-intensive products) by developing a multiregional input-output model. Our model characterizes the impact of international trade between China and 140 economies and China's interprovincial trade on hazardous waste generated by 161,599 Chinese enterprises. We find that, in 2015, virtual hazardous waste flows in China's trade reached 26.6 million tons (67% of the national total), of which 31% were generated during the production of goods that were ultimately consumed abroad. Trade-related production is much dirtier than locally consumed production, generating 26% more hazardous waste per unit of GDP. Under the impact of virtual flows, 40% of the waste-intensive production and relevant disposal duty is unequally concentrated in three Chinese provinces (including two least-developed ones, Qinghai and Xinjiang). Our findings imply the importance of expanding the scope of transboundary waste regulations and provide a quantitative basis for introducing consumer responsibilities. This may help relieve waste management burdens in less-developed "waste havens".

The environmental effects of digital economy: evidence from province-level empirical data in China

The environment is the foundation for human existence, and the digital economy has exacerbated the impact of human beings on the environment. Based on data of 31 provinces in China from 2011 to 2020, we used the spatial Durbin model to research the impact of the digital economy on the environment and its spatiotemporal characteristics. We found that the digital economy has a significant positive impact on the environmental effects of the region, and also has a positive spatial overflow. This conclusion still holds after robustness test and endogenous treatment (changing the space weight matrix, applying the instrumental variables, and two-stage least-squares method). Second, we found that environmental effects of the digital economy have a time lag, and the lag decreases gradually over time. Third, we used geographically and temporally weighted regression model and K-means clustering, which shows that digital economy has a strong effect on the environment in western region. The western region may need to increase digital infrastructure construction to achieve better environmental effects. In addition, China needs to upgrade its industrial structure as soon as possible, accelerate technological innovation, and advocate a green lifestyle, so as to realize the coordinated development of human beings and nature.

Green effects of research and development on industrial waste reduction during the production phase: Evidence from China and policy implications

Maintaining public health requires a clean environment; however, some industrial wastes can damage the water, atmosphere, and living environment seriously. To promote green development, policy makers in China have developed and implemented strict environmental regulations to limit the pollutant emissions and improve the environmental quality. Industrial producers implement research and development (R&D) activities to gain more profits in competitive markets. A comprehensive understanding of the green effects of R&D on different industrial wastes could provide important policy recommendations, especially regarding the coordination of innovative and green developments. In this study, the author empirically analyzed the influence of R&D input, including the intramural expenditure on R&D and full-time equivalent of R&D personnel, on industrial wastes, including the discharge of chemical oxygen demand (COD) and ammonia nitrogen, emission of sulfur dioxide, nitrogen oxides, and particulate matter, and generation of common industrial solid and hazardous wastes, based on the data from Chinese industrial sectors for 2016-2020. The main findings of empirical analyses were robust and indicated that R&D activities significantly reduced the emissions of all three industrial waste gases and decreased the discharge of COD; however, in the case of China, the partial effects on the discharge of ammonia nitrogen and the industrial solid wastes were not statistically significant. The green effects of R&D on different industrial wastes may vary and generally depend on environmental regulations, with various limitations. The most viable policy recommendations indicate that by expanding and initiating the green effect of R&D on different industrial wastes, innovative and green developments are more likely to be achieved in a coordinated manner. Additionally, this can also support special R&D activities, with the added benefit of actively developing cleaner technology to treat pollutant emissions. Development, while maintaining a clean environment to ensure public health, could be more sustainable if innovative activities reduce the production of industrial wastes. This study analyzes the green effects of R&D on industrial waste and can serve as a viable framework for future studies on sustainable development.

Characteristics of industrial hazardous waste generation in South Korea from 2008 to 2018 based on decoupling and decomposition analysis

The industrial hazardous waste (IHW) generation to meet consumption is steadily increasing, resulting in environmental, health, and social problems around the world. To address IHW at the source, it is critical to understand the generation characteristics and key drivers on industrial hazardous waste generation (IHWG). This study analysed the generation characteristics of IHW of South Korea from 2008 to 2018 by decoupling and index decomposition analysis using Log Mean Divisia Index (LMDI) model. South Korea presented unstable decoupling of IHWG from economic growth, so more effective waste management regulations are needed to support a stable decoupling. One most critical finding was that the factors of industrial output and industrial characteristic of IHWG-to-energy were major driving factors influencing the increase of IHWG, whereas those of industrial structure and energy efficiency affect to the decrease of IHWG in most industries. In addition, the result clearly confirmed that the contribution of driving factors affecting the IHWG differs by industry. These results provide significant policy insights that the South Korean government needs institutional improvement and refinement of customised IHW management according to the characteristics of IHWG.

Microbial Pretreatment of Chicken Feather and Its Co-digestion With Rice Husk and Green Grocery Waste for Enhanced Biogas Production

To utilize wastes and residues sustainably and excellently, there is a need to fend for efficient methods and resources for biogas production. Use of poultry waste for biogas production represents one of the most important routes toward reaching global renewable energy targets. The current study involves microbial pretreatment of chicken feather waste, followed by its co-digestion with rice husk and green grocery waste in batch and continuous reactors, respectively. Microbial pretreatment of chicken feathers by keratinase secreting Pseudomonas aeruginosa was an effective and eco-friendly approach to make its recalcitrant structure available as a raw substrate for biogas production. The current study also addressed the enhancement and stability of anaerobic digestion by co-digestion. Results demonstrated that biogas production was increased by microbial pretreatment of chicken feathers and that the percentage increase in biogas yield was 1.1% in microbialy pretreated feathers compared to mono-digestion (non-pretreated feathers) in batch fermentation. The highest yield of biogas was obtained in a batch reactor having co-digestion of pretreated rice husk and microbial pretreated chicken feathers. The co-digestion of chicken feathers hydrolysate with green grocery waste in continuous fermentation mode has also enhanced the biogas yield as compared to average of mono-digestion (chicken feather hydrolysate and green grocery waste) and, therefore, improve the efficiency of the overall process.

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.

Assessment of physical quantity and value of natural capital in China since the 21st century based on a modified ecological footprint model

Natural capital accounting is an essential prerequisite for the harmonious development of human beings and nature and benign interactions between economy and environment. Although the ecological footprint has significantly contributed to sustainability evaluation for many years, the traditional ecological footprint model is limited by lack of value accounting, incomplete account content, neglect of multi-functions of land, and geographical spatial heterogeneity. These limitations reduce the universality of this model. To improve the value accounting of the ecological footprint model, this study takes national hectares (nha) as the measurement unit and includes the freshwater and pollution footprints. The dynamic changes of natural capital from 2000 to 2018 were calculated and analyzed in 31 Chinese provinces. The main findings are summarized here. China's physical quantity of ecological footprint, ecological carrying capacity, and ecological deficit in 2018 was 4.03, 0.79, and -3.24 billion nha, respectively. The energy account contributed most of the physical quantity in the ecological footprint (72.12% of the total). From 2000 to 2018, the physical quantities of the per capita ecological footprint and the per capita ecological deficit increased at 5.49% and 10.08% per annum, respectively, while the physical quantity of the per capita ecological carrying capacity decreased by 0.55% per annum. The physical quantities of the per capita ecological footprint and per capita ecological deficit were spatially distributed, reducing in the order of East > Central > West. The spatial distribution of the physical quantity of the per capita ecological carrying capacity showed the opposite trend. By 2018, China's ecological footprint, ecological carrying capacity, and ecological deficit were valued at 18.09, 12.44 and CNY -5.65 trillion, respectively. Over the 2000-2018 period, the per capita ecological footprint and ecological carrying capacity increased by 495 and CNY 370 per annum, respectively, while the per capita ecological deficit expanded at CNY -125 per annum. The biological account contributed approximately 59.53% of the value quantity of the ecological footprint. Since the twenty-first century, the cumulative effect of excessive consumption has placed increasing pressure on China's ecosystems. On mainland China, only Tibet showed an ecological surplus in 2018. The ecological pressure index decreased in the order of East > Central > West. As Western China has developed extensively and its ecological deficit is rapidly expanding, this region deserves special attention. The most ecologically challenging regions in China are Shanghai, Tianjin, and Beijing. In contrast, Jilin, Qinghai, and Tibet impose low ecological pressure in China. These findings contribute to the standardization and localization of the ecological footprint model in China and provide a reference for regional resource management and ecological construction.

Microstructure Evolution and Improved Permeability of Ceramic Waste-Based Bricks

The resource and large-scale utilization of waste ceramic materials, magnesium slag, and coal gangue are one of the important ways for the sustainable development in metallurgy, coal, and other related enterprises. In this paper, waste ceramic materials were used as aggregates; coal gangue and magnesium slag were used as mixed binder; and the all solid-waste-based permeable bricks with excellent performance were prepared by forming pressure at 5 MPa. The mechanical properties and water permeability of the all-solid-waste-based permeable bricks were evaluated. The results proved that the porous channel of permeable brick is mainly composed of waste ceramic materials with a particle size of 2–3 mm. Pore structures below 200 μm were mainly composed of fine aggregate and mixed binder. Using 60% coarse aggregate, 20% fine aggregate, 10% coal gangue, and 10% magnesium slag as raw materials, the all-solid-waste-based permeable bricks were obtained by pressing at 6 MPa and sintering at 1200 °C, which exhibited the best performance, and its water permeability, compressive strength, and apparent porosity were 1.56 × 10⁻² cm/s, 35.45 MPa, and 13.15%, respectively. Excellent water permeability, compressive strength, and apparent porosity of the all solid-waste-based permeable bricks were ascribed to the high content of connecting open pores, and closely adhesive force were ascribed to the porous microstructure constructed by the grading of waste ceramic materials and the tight conjoined points of the liquid phases in coal gangue and magnesium slag at a high sintering temperature.

Decomposition analysis of the decoupling and driving factors of municipal solid waste: Taking China as an example

As one type of debt 'borrowed' from nature, municipal solid waste (MSW) can be influenced by financing debt. Taking China as an example, an improved Logarithmic Mean Divisia Index (LMDI) model, together with the Tapio decoupling model, is developed to analyze the impact of private debt on MSW generation and the relationship between MSW and economic growth. The results show that the debt-income ratio promotes MSW generation and the output efficiency of debt inhibits MSW generation. Second, the linkage relationship between GDP growth and MSW shows three states: strong decoupling, expansion coupling and weak decoupling. The MSW generation per unit of GDP and the output efficiency of debt are the main contributors to the change of decoupling state. Third, implementing a MSW classification measure can greatly reduce the quantity of MSW removed and transported and improve the decoupling state. By 2035, deleveraging scenario and economic growth slowdown scenario can reduce MSW removal and transportation quantities by 765 and 1080 million tons, respectively. It is worth noting that negative population growth worsens decoupling while curbing MSW. The results provide a new perspective for the realization of MSW reduction and some sound policies are formulated to improve MSW management.

Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models

Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.