Elsevier

Journal of Cleaner Production

Volume 235, 20 October 2019, Pages 1450-1464
Journal of Cleaner Production

Identifying the driving factors of energy-water nexus in Beijing from both economy- and sector-wide perspectives

https://doi.org/10.1016/j.jclepro.2019.07.001Get rights and content

Highlights

  • Service and urban household were the key EW nexus sectors.

  • Production and population expansion considerably increased both EW use.

  • There existed synergic effects of various factors on the reduction of EW use.

  • Modern agricultural technologies caused trade-off between energy- and water-saving.

Abstract

This study develops an integrated analytical framework, based on LMDI method, to identify the driving factors of energy and water (EW) nexus in Beijing from both economy- and sector-wide perspectives. The notable findings are: (1) The service and urban household sectors contributed most to the increase of energy use while the agriculture sector played a dominated role in the large decline of water use in Beijing from 2002 to 2017. Service and urban household were the key sectors of EW nexus. (2) Both economy- and sector-wide factors exerted synergistic effects on EW use, although their contributions to water-saving were much higher than energy-saving. Regarding economy-wide factors, production expansion considerably increased both EW use in Beijing, with the contribution degrees of 129.9% and 276.2%, respectively, and population expansion also played an important role in increasing EW use, with the contribution degree of 10.3% and 32.9%; while efficiency improvement and industrial structure adjustment led to much more water-saving of 261.7% and 145.1% than energy-saving of 56.6% and 5.6%, respectively. As to sector-wide factors, production expansion of industry and service sectors, and income improvement of urban household contributed to a considerable increase in both energy and water use, and the contribution degree are 58.3%, 65.7% and 28.5% for energy use as well as 61.0%, 79.8% and 22.8% for water use, respectively, whereas efficiency improvement of industry sector effectively curbed the increase of energy and water use, with the contribution degree of −50.5% and −76.7%, respectively. (3) The only trade-off between energy- and water-saving occurred in the efficiency improvement of the agriculture sector. (4) More attention should be paid to reducing EW use in the service and urban household sectors through efficiency improvement and behavior guidance for EW saving.

Introduction

Energy and water are indispensable inputs to support modern socioeconomic growth and human survival. Along with rapid economic growth and urbanization, excessive exploitation and utilization of energy and water resources have become important environmental issues in China, which hinders economic growth in turn. China's energy and water resources are unevenly distributed, with an energy-rich north and western and a water-abundant south. Domestic large-scale energy transfer, such as West-to-East natural gas transportation project, and water diversion, such as South-to-North water diversion project (SNWDP), further aggravate local energy shortage and water scarcity in cities (Chu et al., 2019). Taking the SNWDP as an example, the largest inter-basin transfer project mitigates the water scarcity in northern cities, but also increases energy use enormously for water abstraction, treatment and supply in the southern energy-shortage cities. Therefore, it is urgent to handle energy and water resource issues properly and timely to achieve urban sustainability in China (Yang et al., 2018).

Energy and water are inextricably linked. Water is used for energy mining, oil refining, and energy conversion, while energy is used for water pumping, raw water treatment, distribution, end use, wastewater collection and treatment (Grubert and Webber, 2015). Electricity can be generated from water and wastewater flows that contains a large amount of thermal and chemically bound energy (Chhipi-Shrestha et al., 2017a, Chhipi-Shrestha et al., 2017b). Traditionally, this interrelationship or interdependence between energy and water could be defined as EW nexus (Scott et al., 2011). Extensive studies have explored energy use for water supply and wastewater treatment (Fang et al., 2015), water use for power generation (Li et al., 2015), and oil extraction (Laurenzi et al., 2016). However, EW nexus were further explored from the aspects of the quantities of national and sectoral EW use (Fan et al., 2019) as well as the trade-offs (Qin et al., 2015), spillover effects (Zhou et al., 2016), synergies (Shang et al., 2018), co-benefits (Gao et al., 2019) of various EW-related measures. Many studies analyzed EW use in the agriculture (Martin-Gorriz et al., 2014), iron and steel (Wang et al., 2017a), paper (Ma et al., 2018), service (Becken and McLennan, 2017) and household sectors (Escriva-Bou et al., 2015). Some researchers evaluated the interactions of energy- or water-related polices or measures, such as renewable energy deployment (Arent et al., 2014), and energy tax collection (Zhou et al., 2016), mandatory energy consumption cap (Shang et al., 2018), energy efficiency improvement (Zhou et al., 2018), as well as water supply structure improvement (Stokers and Horvath, 2009), water price mechanism (Zhou et al., 2013), and mandatory water conservation (Spang et al., 2018). Most current studies focused on an isolated sector although some researches were involved in EW nexus of multi-sector or economy-wide EW nexus from the urban (Wang and Chen, 2016), regional (Wang et al., 2018), and national perspectives (Xiang, 2019).

In addition, Chen and Lu (2015) emphasized the dynamic nature of “being” of nexus. They also pointed out that socio-economic factors, such as economic growth and urbanization, have been incorporated into the urban nexus paradigm, and many studies evaluated how these intertwined factors, affected the urban development from a nexus perspective. To date, EW nexus is not limited to the interdependency of energy for water and water for energy. The implication of EW nexus has been extended as the dynamic EW interrelationship including the interdependency between energy and water, energy and water use of non-energy and -water sectors, and the effects of socio-economic factors and EW-related policies or measures on EW use in terms of the spillover effect, synergies, co-benefits and trade-offs.

Most existing studies focus on the assessment of the dynamic impacts of EW-related policies, although there were some studies evaluating dynamic effects of socio-economic factors (except EW-related policies) on EW use at regional and city levels, such as urban size (Chen and Chen, 2017), population intensity (Chhipi-Shrestha et al., 2018), domestic trade (Nawab et al., 2019), international trade (White et al., 2018) and industrial structure adjustment (Lin et al., 2019). For example, Chen and Chen (2017) took 66 urban samples explored the correlation between energy consumption and CO2 emissions with consideration of urban size and population density from 1865 to 2014. In additions, current studies largely considered EW nexus of urban sectors from a static perspective, such as Chen and Chen, 2016, Wang et al., 2017b and Yang et al. (2018). To our knowledge, this is the first study to analyze urban EW nexus that includes the socio-economic factors of urbanization and income improvement, and to evaluate the impacts of urban sector-level socio-economic factors from a dynamic perspective. Therefore, this study aims at developing an integrated framework to reveal the dynamic changes of EW nexus characteristics, and then quantifies the contributions of various socio-economic factors. The dynamic perspective of EW nexus and the impact assessment of socio-economic factors on EW nexus in this study could perform well in representing and revealing urban EW nexus.

Many methods have been used to investigate EW nexus issues, such as life cycle analysis (LCA) (Plappally and Lienhard, 2012), system dynamic (SD) model (Feng et al., 2016), input-output (IO) analysis (Wang et al., 2017c), CGE model (Zhou et al., 2018) and martial flow analysis (MFA) (Lee et al., 2018). However, IO analysis, LCA and MFA are usually used for EW flow accounting. IO analysis, as one of the most popular methods for EW nexus studies in existing literatures (shown in Table 1), performs well in investigating the interrelationship in terms of EW flow among sectors within an economy and among regions (Chen et al., 2018). However, IO analysis cannot directly capture the effects of socio-economic factors and EW-related policies. Lots of parameter hypothesis increases the uncertainty of simulated results in CGE and SD model. Differentiated setting of multi-sectoral EW nexus increases the difficulties of the development of CGE model and the calculation process significantly. SD model would produce chaos and bifurcation if system is complex (Feng et al., 2016). Structural decomposition analysis (SDA), was also employed to explore energy consumption (Zhang et al., 2015), water use (Zhi et al., 2016), CO2 emissions (Chen and Zhu, 2019), and their nexus issue (Lin et al., 2019). SDA method is highly dependent on the availability of IO table (Wang et al., 2017b), while logarithmic mean Divisia index (LMDI) decomposition method, a kind of index decomposition analysis, is less data-intensive and more diverse in decomposition forms than SDA (Zhao et al., 2016). Therefore, LMDI could be a preferred approach for quantifying the effects of various driving factors, such as GDP, industrialization, urbanization and population, on EW nexus, and this assessment is simpler in model development and calculation than CGE and SD models.

Many literatures, based on LMDI decomposition method, explored the driving forces of energy consumption (Du and Lin, 2015), water use (Xu et al., 2015) and CO2 emissions (Li et al., 2017) from global, national, regional and sectoral perspectives. Recently, LMDI was popularly applied to explore the factors affecting energy-related CO2 emissions (Chen and Zhu, 2019) and water use (Wang and Li, 2018) at the city level. Using LMDI method, Zhang et al. (2018) identified the driving factors behind changing water use patterns, and revealed the spatial distribution of thermoelectric water stress in China during 2000–2015. So this study adopts LMDI method to investigate the driving factors of dynamic changes of EW use and EW nexus characteristics.

The integrated management of EW resources requires to understand the major driving factors of the changes of EW nexus characteristics to achieve urban sustainability of EW supply. Using LMDI decomposition method, this study develops an integrated framework to evaluate how socio-economic factors affect urban EW nexus characteristics like Beijing. EW nexus characteristics in this study are measured in terms of EW use of urban sectors, as well as the synergies and the trade-offs of various socio-economic factors on EW use. In this study, socio-economic factors include economy- and sector-wide factors: economy-wide factors are involved in both production and household sectors within an economy and they work through joint effects of all urban sectors, while sector-wide factors work due to the efforts of a specific sector. For example, overall energy efficiency improvement of all urban sectors is an economy-wide factor while energy efficiency improvement of industry sector is a sector-wide factor. Then, the integrated framework was applied to analyze the driving factors of EW nexus of Beijing from 2002 to 2017 to offer policymakers references for taking integrated EW governance measures.

This study may contribute to existing literature from the following aspects. First, the developed model framework could be employed to analyze the driving factors and their effects of economy- and sector-wide factors on EW nexus from a dynamic perspective, which caters to the nature of nexus. Second, the original LMDI method was improved, including the factors of industrialization, urbanization and income improvement, and then was employed to analyze EW nexus issue. It extends the application of LMDI method. Third, the results could provide a reference for decision-making in integrated management of EW resources in Beijing. Although this study only considers two year of 2002 and 2017, this comparison of sectoral EW use between 2002 and 2017 offers holistic understanding of EW nexus characteristics of urban sectors. The impact assessment of various economy- and sector-wide factors on EW nexus contributes to the macro policy design of EW management of urban sectors.

The rest of this study is organized as follows. Section 2 displays the model framework developed for EW nexus analysis and data source used in this study, and Section 3 introduces the overall socioeconomic development and EW nexus characteristics of Beijing. Then the model framework is applied to investigate the EW nexus changes and assess the contributions of various driving factors, and the results are displayed in Section 4. Section 5 draws the main conclusions of this study and future research direction of urban EW nexus.

Section snippets

Model framework

Fig. 1 shows the model framework of identifying the driving factors of the changes of EW nexus characteristics, and the variables used in this study are shown in Table A1. First, EW use of various sectors in 2002 and 2017 are compiled, respectively. Six main sectors, including agriculture, industry, construction, service, urban household, and rural household sectors, are considered according to the major sector classification of an economy and data availability. Second, LMDI method is employed

Socioeconomic development condition and EW nexus characteristic in Beijing

In this study, we focus on the EW nexus characteristic and the driving factors of the change of EW nexus in Beijing. Beijing is chosen as the study area because Beijing is the capital, representative major city and international metropolis in China. More importantly, Beijing is faced with severe energy and water resource shortage issue, due to rapid economic growth and large population while lower resource endowment and heavy water pollution (Shao et al., 2017). In Beijing, 22% of direct water

Effects of economy-wide factors on energy use

From 2002 to 2017, energy use of Beijing increased by 40.2 Mtce. As shown in Fig. 5, the production scale effect contributed to the largest increase in energy use of 52.2 Mtce while energy intensity effect in the production sector reduced energy use by 22.7 Mtce. Beijing was one of the most developed and fast-growing city in China, and rapid production scale expansion increased energy use of Beijing significantly. Energy efficiency gained great improvement in Beijing from 2002 to 2017. Both

Conclusions

This study develops an integrated analytical framework to explore the EW nexus characteristics and identifies the driving factors of EW nexus from a dynamic perspective. This integrated framework is applied to investigate the key driving factors of the change of EW nexus characteristics of Beijing from 2002 to 2017, in order to offer data reference for Beijing to design integrated and targeted EC-related policies or measures. Main findings are as follows:

  • (1)

    In the past fifteen years, Beijing

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this study.

Acknowledgements

This study was financially supported by the Beijing Natural Science Foundation (No. 9172015), Beijing Social Science Foundation (No. 17JDYJB010), Joint Development Program of Beijing Municipal Commission of Education and China Scholarship Council (201806030178).

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