Exploring Spatial Heterogeneity and Factors Influencing Construction and Demolition Waste in China

Abstract Natural disasters, new urbanization and urban renewal activities generated a large amount of construction and demolition waste (C&DW), and managing C&DW has become an urgent problem to be solved in the construction of “Zero-waste cities”. Based on the calculation of C&DW generation in China from 2005 to 2019, this study analyzed the temporal and spatial characteristics of C&DW generation in China, and empirically explored the factors influencing factors C&DW of China using spatial autocorrelation and geographically weighted regression. The results showed that: (1) C&DW generation in China increased every year, and the overall distribution was characterized as “high in the east and low in the west”, with distinct regional differences. The provinces with the highest per capita C&DW generation were Zhejiang, Jiangsu, Beijing, Shanghai, and Fujian. The generation intensity of C&DW in China and all its provinces showed a decreasing trend every year. The regions with rapid growth of C&DW generation in China were concentrated in the eastern coastal areas, with distinct differences between the east and west, and there was significant spatial heterogeneity in the growth trend. (2) There is a significant spatial autocorrelation in C&DW generation in China. Overall, the hot spots for C&DW generation were distributed in Jiangsu, Zhejiang, and Shandong provinces, and the spatial agglomeration effect of C&DW generation in provinces was evident. (3) Factors such as population size, per capita Gross Domestic Product and the scale of the construction industry played a positive role in promoting C&DW generation in each province, whereas labor efficiency played a negative role inhibiting C&DW generation; there was significant temporal and spatial heterogeneity. Finally, differentiation and cross-regional joint treatment strategies according to regional conditions were proposed to achieve precise measures of C&DW reduction management.

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Spatial autocorrelation, influencing factors and temporal distribution of the construction and demolition waste disposal industry

Waste Management ◽

10.1016/j.wasman.2021.04.025 ◽

2021 ◽

Vol 127 ◽

pp. 158-167

Author(s):

Haizi Wang ◽

Xinming Pan ◽

Shibin Zhang

Keyword(s):

Spatial Autocorrelation ◽

Influencing Factors ◽

Waste Disposal ◽

Temporal Distribution ◽

Construction And Demolition Waste ◽

Demolition Waste

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Generation and Prediction of Construction and Demolition Waste Using Exponential Smoothing Method: A Case Study of Shandong Province, China

Sustainability ◽

10.3390/su12125094 ◽

2020 ◽

Vol 12 (12) ◽

pp. 5094 ◽

Cited By ~ 1

Author(s):

Liang Qiao ◽

Doudou Liu ◽

Xueliang Yuan ◽

Qingsong Wang ◽

Qiao Ma

Keyword(s):

Average Rate ◽

Estimation Method ◽

Prediction Method ◽

Exponential Smoothing ◽

Shandong Province ◽

Construction And Demolition Waste ◽

Trend Test ◽

Growth Trend ◽

Demolition Waste ◽

Current Output

The output of construction and demolition (C&D) waste in China has been rapidly increasing in the past decades. The direct landfill of such construction and demolition waste without any treatment accounts for about 98%. Therefore, recycling and utilizing this waste is necessary. The prediction of the output of such waste is the basis for waste disposal and resource utilization. This study takes Shandong Province as a case, the current output of C&D waste is analyzed by building area estimation method, and the output of C&D waste in the next few years is also predicted by Mann–Kendall trend test and quadratic exponential smoothing prediction method. Results indicate that the annual productions of C&D waste in Shandong Province demonstrates a significant growth trend with average annual growth of 11.38%. The growth rates of each city differ a lot. The better the city’s economic development, the higher the level of urbanization, the more C&D waste generated. The prediction results suggest that the output of C&D waste in Shandong Province will grow at an average rate of 3.07% in the next few years. By 2025, the amount of C&D waste will reach 141 million tons. These findings can provide basic data support and reference for the management and utilization of C&D waste.

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Stakeholder-Associated Factors Influencing Construction and Demolition Waste Management: A Systematic Review

Buildings ◽

10.3390/buildings11040149 ◽

2021 ◽

Vol 11 (4) ◽

pp. 149

Author(s):

Xianbo Zhao

Keyword(s):

Systematic Review ◽

Waste Management ◽

Associated Factors ◽

Influential Factors ◽

Construction And Demolition Waste ◽

Journal Articles ◽

Demolition Waste ◽

Roles And Responsibilities ◽

Factors Influencing ◽

Specific Factors

Construction and demolition (C&D) activities generate a large amount of waste and have significant impacts on the environment. Thus, it is necessary to implement C&D waste management (WM), which requires the involvement of stakeholders and is influenced by a variety of factors. This study aims to undertake a systematic review of the stakeholder-associated factors influencing C&D WM. The Scopus search engine was used in a literature search, and two rounds of screening were performed. Only journal articles or reviews that were published in English after 2000 were used in this study. A total of 106 journal articles were reviewed. The review identified 35 stakeholder-associated factors influencing C&D WM and categorized them into six groups: regulatory environment, government and public supervision, advances in technologies, recycling market, knowledge, awareness, attitude, and behaviour of stakeholders, and project-specific factors. All the 35 factors are discussed in detail with considerations into relevant stakeholders. Although there have been studies focused on the factors influencing C&D WM, few have attempted to take stakeholders’ perspectives into consideration. This study expands the C&D WM literature by mapping the influential factors with relevant stakeholders and enables the practitioners to clearly understand their roles and responsibilities and make better informed decisions in the C&D WM process.

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BIM for Deconstruction: An Interpretive Structural Model of Factors Influencing Implementation

Buildings ◽

10.3390/buildings11060227 ◽

2021 ◽

Vol 11 (6) ◽

pp. 227

Author(s):

Lovelin Obi ◽

Bankole Awuzie ◽

Chukwudi Obi ◽

Temitope S. Omotayo ◽

Adekunle Oke ◽

...

Keyword(s):

Construction Industry ◽

Business Models ◽

Structural Model ◽

Factor Model ◽

Construction And Demolition Waste ◽

Successful Implementation ◽

Demolition Waste ◽

Factors Influencing ◽

The Uk ◽

Interpretive Structural Model

Transitioning from demolition to deconstruction practices for end-of-life performances is gaining increasing attention following the need for the construction industry to minimise construction and demolition waste. Building information modelling (BIM) presents an opportunity for sustainable deconstruction. However, the notion of BIM for deconstruction (BIMfD) is still in its infancy in the United Kingdom. Although a few studies on BIMfD are evident, a focus on identifying the underlying factors necessary for successful implementation of BIMfD is lacking. The purpose of this study was to identify and analyse the underlying factors necessary for BIMfD implementation in the UK construction industry. It employed a four-stage research design. The reviewed literature explored extant views on BIM implementation factors to identify an initial list of possible factors influencing BIMfD implementation. Subsequently, a mix of questionnaire, focus group discussions and structured interviews were employed at various stages to refine and contextualise 15 factors necessary for BIMfD implementation in the UK construction industry. The contextual interrelationships among the factors were evaluated using interpretive structured modelling (ISM). This evaluation culminated in a BIMfD implementation factor model. The findings identified BIMfD experts, responsiveness of business models to innovative practices and industry’s acceptance to embrace change as the principal factors influencing BIMfD implementation in the UK. The implications of the findings attest that BIMfD experts and advisors must champion the adoption and implementation of BIMfD in the UK and business models need to become more responsive to accommodate BIMfD innovative practices. A BIMfD framework was conceptualised. Even though the BIMfD framework was designed from the UK perspective, the global construction industry can leverage the outcomes of this study. This paper, therefore, brings to the fore, a hierarchical BIMfD implementation factor model to support improved deconstruction practices in the construction industry.

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Spatial Heterogeneity of the Impact Factors on Gray Water Footprint Intensity in China

Sustainability ◽

10.3390/su12030865 ◽

2020 ◽

Vol 12 (3) ◽

pp. 865

Author(s):

Lingling Zhang ◽

Rui Zhang ◽

Zongzhi Wang ◽

Fan Yang

Keyword(s):

Spatial Heterogeneity ◽

Pollution Control ◽

Total Population ◽

Water Footprint ◽

Factors Influencing ◽

Gray Water ◽

Per Capita ◽

Urbanization Rate ◽

The Impact ◽

Capita Output

The gray water footprint intensity represents the amount of freshwater resources that need dilution of pollutants per unit of economic output, which indicates the relationship among water pollution, water resources and economy. In this paper, the gray water footprint of 31 provinces (autonomous regions) in China was estimated based on different water bodies. The spatial pattern and spatial agglomeration characteristics of gray water footprint in China from 2000 to 2014 were explored from the perspective of spatial autocorrelation. By extending the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact of the total population, urbanization rate, per capita output value, the proportion of the tertiary industry, environmental pollution control intensity and R&D investment intensity on the degree of gray water footprint intensity are explored, with ridge regression analysis to solve the problem of collinearity affecting factors. Meanwhile, the Geographically Weighted Regression (GWR) model is used to detect the spatial heterogeneity and spatio-temporal variation characteristics of the factors influencing gray water footprint intensity among regions. The study found that from 2000 to 2014, the gray water footprint of 31 provinces and cities in China was unstable; the domestic gray water footprint accounted for the largest proportion; the agricultural gray water footprint was mainly derived from nitrogen fertilizer, and the industrial and domestic gray water footprint was mainly derived from ammonia nitrogen. Water pollution varies from east to west. The total intensity of gray water footprint shows a downward trend, which is related to economic development and improvement of technological level. There is a positive correlation between the urbanization rate and the intensity of the gray water footprint. The total population, the per capita output value, the proportion of the tertiary industry, the intensity of environmental pollution control, the intensity of R&D input and the intensity of the gray water footprint are negatively correlated, and the influencing factors boast obvious spatial heterogeneity. The purpose is to reveal the key factors influencing gray water footprint intensity to ensure the sustainable development of economy, resources and environment through the formulation of regional differences in regulation and control policies.

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Reproduction potential-based differentiation of Russian regions

Regional Economics Theory and Practice ◽

10.24891/re.18.7.1304 ◽

2020 ◽

Vol 18 (7) ◽

pp. 1304-1319

Author(s):

M.V. Moroshkina

Keyword(s):

Spatial Development ◽

Uneven Development ◽

Regional Differentiation ◽

Russian Regions ◽

Policy Documents ◽

Factors Influencing ◽

Strategic Policy ◽

Main Factors ◽

Per Capita ◽

Research Period

Subject. This article examines the issues related to changes in reproduction capacity and heterogeneity of the development of Russian regions. Objectives. The article aims to assess regional differentiation and investigate the main factors influencing the uneven development of the areas. Methods. For the study, we used the methods of comparative and correlation analyses. Results. The article identifies groups of leading and lagging Russian regions and assesses the possibility of convergence of Russian regions according to the analyzed indicators, such as GRP, GRP per capita, and the output of industry. Conclusions. The results obtained can be used when preparing strategic policy documents, spatial development programmes and concepts. The observed heterogeneity suggests that the regions maintain their positions throughout the research period.

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