General Equilibrium Analysis of the Cobenefits and Trade-Offs of

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A general equilibrium analysis of co-benefits and trade-offs of carbon mitigation on local industrial water use and pollutants discharge in China Qiong Su, Hancheng Dai, Huan Chen, Yun Lin, Yang Xie, and Raghupathy Karthikeyan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b05763 • Publication Date (Web): 08 Jan 2019 Downloaded from http://pubs.acs.org on January 11, 2019

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A general equilibrium analysis of co-benefits and trade-offs of carbon mitigation on local

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industrial water use and pollutants discharge in China

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Qiong Sua, Hancheng Daib, *, Huan Chenc, Yun Lind, Yang Xiee, Raghupathy Karthikeyana,f

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aDepartment

of Water Management & Hydrological Science, Texas A&M University, College

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Station, Texas 77843, USA

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bCollege

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cBelle

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USA

of Environmental Sciences and Engineering, Peking University, Beijing 100871, China

W. Baruch Institute of Coastal Ecology and Forest Science, Clemson University, SC 29442,

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dAtmospheric

Science and Global Change Division, Pacific Northwest National Laboratory

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Richland, WA, USA

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eSchool

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fDepartment

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Texas 77843, USA

of Economics and Management, Beihang University, Beijing 100191, China of Biological and Agricultural Engineering, Texas A&M University, College Station,

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* Corresponding Author, Email address: [email protected] (H.C., Dai) Room 246,

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Environmental Building, College of Environmental Sciences and Engineering, Peking University,

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Beijing 100871, China; TEL/Fax: (+86) 10-6276-4974

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Author contributions: Q.S. and H.C.D designed the research and developed the integrated model;

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Q. S., H.C.D., H.C., Y.L., X.Y., R.K., analyzed data; Q.S., H.C.D wrote the paper.

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Abstract:

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Carbon mitigation strategies have been developed without sufficient consideration of their impacts

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on the water system. Here, our study evaluates whether carbon mitigation strategies would decrease

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or increase local industrial water use and water-related pollutants discharge by using a computable

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general equilibrium (CGE) model coupled with a water withdrawals and pollutants discharge

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module in Shenzhen, the fourth largest city in China. To fulfill China’s Nationally Determined

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Contributions (NDC) targets, Shenzhen’s GDP and welfare losses are projected to be 1.6% and

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5.6% in 2030, respectively. The carbon abatement cost will increase from 56 USD/t CO2 in 2020

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to 274 USD/t CO2 in 2030. The results reveal that carbon mitigation accelerates local industrial

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structure upgrading by restricting carbon-, energy-, and water-intensive industries, e.g., natural gas

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mining, non-metal, agriculture, food production, and textile sectors. Accordingly, carbon

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mitigation improves energy use efficiency and decreases 55% of primary energy use in 2030.

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Meanwhile, it reduces 4% of total industrial water use and 2.2-2.4% of two major pollutants

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discharge, i.e., CODCr and NH3-N. Carbon mitigation can also decrease petroleum (2.2%) and V-

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ArOH (0.8%) discharge but has negative impacts on most heavy metal(loid)s pollutants discharge

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(increased by -0.01% to 4.6%). These negative impacts are evaluated to be negligible on the

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environment. This study highlights the importance of considering the energy-water nexus for

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better-coordinated energy and water resources management at local and national levels.

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Keywords: Energy-Water Nexus, CO2 emission control, IMED|CGE model, discharge reduction,

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China

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1. INTRODUCTION

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With continuous population and economic growth, China has become one of the largest

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greenhouse gas emitters since 2006.1 To mitigate climate change impacts, the Chinese government

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announced its Nationally Determined Contributions (NDC) in the Paris Agreement to reduce

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carbon dioxide (CO2) emissions per unit of GDP by 60% to 65% in 2030 compared with 2015

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level.2 The implementation of China’s NDC is supposed to improve energy use efficiency and

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accelerate industrial structure upgrading by decreasing the proportion of energy- and carbon-

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intensive industries.3, 4 However, the industry structure upgrading may also have significant impacts

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on water use and pollutants discharge in related industrial sectors considering that energy and water

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are two basic inputs of industrial production.5-7 Given this context, modeling the impacts of China’s

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NDC on energy use, economic development, water use, and water-related pollutants discharge is

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crucial to design effective policies and measures for addressing energy and water issues.

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Until now, energy-water nexus studies have been focusing on assessing the impacts of carbon

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mitigation strategies on water use in a single8-10 or two11 industrial sectors. Carbon mitigation could

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increase water consumption in the U.S. electric sector at both regional12 and national13 scales. But

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the impacts have high uncertainty due to the choice of energy sources and the cooling system14-16,

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e.g., the studies in China showed that carbon mitigation could promote renewable energy

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technologies and reduce water consumption in the power generation sector.17,18 Previous studies

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evaluated carbon mitigation impacts on water use in the power generation sector. The multi-sector

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model like MARKAL was used to simulate the energy and water competition between

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thermoelectric power generation and transportation sectors in the U.S. under different carbon

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mitigation scenarios.11 However, these studies are limited to capture the cross-sector interactions

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and feedbacks among economic, energy, and water system. Therefore, they cannot provide an

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integrated view of how carbon mitigation strategies might affect the economic outputs and water

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use in different industrial sectors.


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On the other hand, the impacts of carbon mitigation strategies on water-related pollutant

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emissions have not been well evaluated. The co-benefits between energy use and pollutant

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emissions in the energy-water nexus have been identified using technology-based bottom-up

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approaches.19, 20 However, the changes in energy and water use and pollutant emissions that may

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result from carbon mitigation are not quantitatively evaluated in these studies. In addition, sufficient

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regional details of sectoral water use and pollutant emissions data are usually unavailable in

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developing countries, such as China. To address these gaps, we evaluate the impacts of China’s

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NDC on local industrial water use and water-related pollutants discharge by coupling a computable

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general equilibrium (CGE) model with a water withdrawals and pollutants discharge module. The

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industrial sectors include primary (e.g., agriculture), secondary (e.g., manufacturing, mining, power

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generation, and construction), and tertiary industries. The CGE model could be used to simulate

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the interactions between macro-economy and environmental system at regional or global scales.21

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This integrated economic and water model could evaluate the impacts of carbon mitigation

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strategies on economic, energy, and water system. Also, the possible co-benefits or trade-offs

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across these systems could be identified to support the designing of effective policies and measures.

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Our study selected Shenzhen as the study area. It is the fourth largest city in China and has

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been chosen as a pilot city to perform China’s NDC. It had a total population of 0.7% of China and

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consumed nearly 4.3% and 3.1% of total petroleum and natural gas use of China in 2007,

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respectively (Table S1). Additionally, it has very limited local water resources and environmental

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capacity and is challenged in fulfilling the national industrial water conservation and main

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pollutants discharge reduction targets (i.e., chemical oxygen demand, CODCr, and ammonia

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nitrogen, NH3-N).22, 23 Here, we aim (1) to assess the impacts of China’s NDC on the total amount

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and intensity of energy use and sectoral CO2 emissions; (2) to evaluate the economic impacts in

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Shenzhen when China’s NDC targets are achieved in 2030; and (3) to quantify the impacts of

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carbon mitigation on local industrial water use and water-related pollutants discharge.

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2. METHODOLOGY

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2.1. Integrated economic and water model

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The integrated model includes 1) a city level CGE model and 2) water withdrawal and

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pollutants discharge module. As shown in Figure 1, the CGE model can be used to estimate CO2

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emissions, energy use, macroeconomic impacts, and detailed economic outputs for each sector

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under different carbon mitigation scenarios. The model provides a comprehensive analysis of the

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macroeconomic costs of carbon mitigation. The selected macroeconomic indicators include Gross

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Domestic Products (GDP), government expenditure, welfare, import, and export. The economic

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outputs for each sector are drivers of the water module so that the impacts of CO2 mitigation

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strategies on sectoral water use and water-related pollutants discharge can be estimated.

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Figure1 Research framework

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2.1.1 The IMED|CGE model

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The CGE model can simulate the future economic system (e.g., industry output, domestics,

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and international trade) and capture the interactions and feedbacks among different industrial

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sectors, final consumers, and rest of the world. The IMED|CGE (Integrated Model of Energy,

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Environment and Economy for Sustainable Development | Computable General Equilibrium)

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model used in this study has been widely used for assessing the economic and environmental

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impacts of China's CO2 mitigation strategies at the city,4, 24 provincial,25, 26 and national3, 27 levels.

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The version of this study is a single-region, recursive dynamic CGE model for Shenzhen City with

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22 economic sectors (Table S2) developed by the Laboratory of Energy & Environmental

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Economics and Policy (LEEEP) at Peking University. The 2007 input-output table and 2007 energy

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balance tables of Shenzhen were used for the base year calibration. The model includes a production 5 ACS Paragon Plus Environment

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block, a market block with domestic, government and household incomes and expenditures blocks,

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and international transactions, which is similar to the one-region version.28 The outputs for each

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sector follow a nested constant elasticity of substitution (CES) production function. Inputs include

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material commodities, energy commodities, labor, capital, and resources (including water use).

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More details are included in Supporting Information, in addition, an up-to-date technical

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introduction is available at http://scholar.pku.edu.cn/hanchengdai/imedcge.

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2.1.2 Water withdrawals and pollutants discharge module

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Once the economic outputs for each sector associated with different scenarios are known from

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CGE simulations, the effects of CO2 mitigation strategies on water use and water pollutants

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discharge can be estimated by multiplying time-evolving water intensity and pollutant discharge

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intensity, as shown in Eqs. (1) and (2).

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𝑊𝑡 = ∑𝑖𝑊𝑡𝑖 = ∑𝑖𝑂𝑈𝑇𝑡𝑖 × 𝑊𝐼𝑡𝑖 (1)

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𝑃𝑡 = ∑𝑖∑𝑗𝑃𝑡𝑖𝑗 = ∑𝑖∑𝑗𝑂𝑈𝑇𝑡𝑖 × 𝑃𝐼𝑡𝑖𝑗 (2)

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where, t is time (year); OUTti is the output of ith industry sector (million dollars); Wt and Pt represent

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the total water withdrawals and pollutants discharge in year t; 𝑊𝑡𝑖 and 𝑊𝐼𝑡𝑖 are water withdrawals

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(million tons) and water intensity (water withdrawals per unit output, m3 per million dollar) of the

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ith industry sector, respectively. 𝑃𝑡𝑖𝑗 and 𝑃𝐼𝑡𝑖𝑗 are pollutant discharge (tons) and pollutant discharge

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intensity (pollutant discharge per unit output, tons/million dollar) of the ith industry sector and the

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jth type of pollutants including CODCr, NH3-N, Petroleum, Volatile phenol (V-ArOH), lead (Pb),

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hexavalent chromium (Cr6+), mercury (Hg), cadmium (Cd), and Arsenic(As).

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Time-evolving water intensity (WI) values were applied22 considering the relatively long-term

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historical changes because of technology improvement, as shown in Eq. (3).

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𝑊𝐼 (𝑡)𝑖 = 𝑊𝐼0𝑖 × exp (𝛼𝑖 × 𝑇) (3) 6 ACS Paragon Plus Environment


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where, WI0i is water withdrawal per unit Output (m3/million dollar) of the ith industry in the initial

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year (2014); T is the number of year since the initial year; 𝛼𝑖 is the exponential rates of water

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intensity of ith industry; and 𝑒𝛼𝑖 is considered the water intensity change rate (WR) of the ith

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industry. Water use, or water withdrawals, are defined as the total amount of water removed from

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water resources. The term ‘water consumption’ used in this study refers to water consumed and not

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available to be reused anymore.

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To reduce the source uncertainty, the four-year mean values of water use intensity in each

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industrial sector were used for the initial year (WI0i,) calculation and were detailed in Table S3. The

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rates of the technology-induced water use intensity change in different industrial sectors (WR) were

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calibrated using regression analysis of the historical data during the period of 2000 and 2015, with

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R2 ranging from 0.78 to 0.98 (except 0.53 in the agricultural sector) (Table S4). Though the

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agricultural sector had a low R2 due to its high variability over time, its impacts are negligible given

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that the water withdrawals from the agricultural sector only accounted for 4% of total water

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withdrawals in 2015 in Shenzhen City.

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A similar procedure was applied to calculate the pollutant intensity (PI) values of different

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pollutants in each sector, as shown in Eq. (4).

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𝑃𝐼 (𝑡)𝑖𝑗 = 𝑃𝐼0𝑖𝑗 × exp (𝛽𝑖 × 𝑇) (4)

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where, PI0ij are the jth pollutant discharge per unit Output (m3/million dollar) of the ith industry in

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the initial year (2014); T is the number of year since the initial year; 𝛽𝑖 is the exponential rates of

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the pollutant discharge intensity of ith industry; and 𝑒𝛽𝑖 is considered the pollutant discharge

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intensity change rate (PR) of the ith industry.

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The initial year pollutant intensity values for each industrial sector were collected from the

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pollutant sources survey of Shenzhen (2011-2014),29 and the mean value and its standard deviation

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for each sector are provided in Table S5. The PR of CODCr was used to represent the technology7 ACS Paragon Plus Environment

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induced wastewater treatment improvement in different industrial sectors since it is the prime water

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pollutant discharge control target in China. The PRs of CODCr were calibrated using regression

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analysis of the historical data during the period of 1996-200730 with R2 ranging from 0.47 to 0.95

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(Table S6). A national dataset (2002-2012) was used in sectors with insufficient data.31 Also, non-

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point source pollution from the agriculture sector is not considered due to its relatively small

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amount compared with emissions from other industrial sectors.6

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2.2. Scenarios

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Two scenarios, including a Business as Usual (BaU) scenario and a Nationally Determined

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Contributions (NDC) scenario, are evaluated. The BaU scenario simulates the economic, energy,

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and water system change without the implementation of explicit carbon mitigation policy or water

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saving policies. The future GDP and population growth rate are based on our previous study22 and

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the 13th Five-Year Plan for economic and social development of Shenzhen,32 as detailed in Table

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1. The annual mean growth rates of GDP and population are set at 9.10% and 0.57% between 2007

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and 2020, and 6.40% and 0.22% between 2020 and 2030, respectively. No CO2 emission intensity

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constraint is considered in the BaU scenario. By contrast, the NDC scenario has a constraint on the

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CO2 emission intensity of Shenzhen, which is set to decrease by 45% in 2020 and 65% in 2030 on

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the basis of 2007 level, fulfilling China’s NDC commitment.2 The other settings in the NDC

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scenario are the same as those in the BaU scenario.

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Table 1. Configurations for the two scenarios.

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3. RESULTS

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3.1. Energy use and CO2 emissions

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Carbon mitigation substantially reduces the local CO2 emissions and energy use, i.e., by 67%

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and 55% in 2030 compared with the BaU scenario, respectively (Figure 2). The total CO2 emissions

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and the primary energy use under the NDC scenario in 2030 are projected to be 140.4 Mt and 0.30

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EJ, respectively. The carbon intensity of Shenzhen in 2007 was 0.81 kg/USD, less than half of the

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mean value of China (1.7 kg/USD). The carbon intensity shows a slightly increasing trend under

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the BaU scenario, but with the carbon emissions constraint, it will decrease by 39% in 2020 and

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64% in 2030, fulfilling the regional carbon intensity reduction target. In addition, the energy

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intensity decreases by 49% in 2030 under the NDC scenario, and the energy intensity reduction

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rate is much larger than the BaU scenario (decreases by 15%). When the constraint is imposed,

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carbon emission allowance becomes a scarce resource, and a carbon shadow price is generated

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endogenously in the CGE model. As explained in our previous study,33 the carbon shadow price,

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or the carbon abatement cost, is considered the marginal cost to achieve the required emission

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reduction target. It is an equilibrium price, which could balance the supply and demand of the

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carbon emission allowance. The supply of carbon emissions is represented by the future GDP and

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carbon intensity target, and the demand is determined by the emissions requirement of different

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industrial sectors and households, which is influenced by the industrial output in different sectors

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and households’ income level, respectively. The carbon abatement cost is evenly distributed in all

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sectors and will increase from 56 USD/t CO2 in 2020 to 274 USD/t CO2 in 2030 because of more

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stringent carbon constraints and adverse endogenous factors such as few available low carbon

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technologies and higher renewable energy prices in Shenzhen. The carbon abatement cost increases

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the production prices of all sectors, which is dependent on the carbon intensity of each sector. On

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the demand side, consumers respond to the price changes and will accordingly adjust their activities

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to decrease the demand for energy- and carbon-intensive products as well as fossil energy. 9 ACS Paragon Plus Environment

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Among all sectors in Shenzhen, the transport, natural gas, residential, power generation,

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construction, services, and electronics sector are the main contributors to CO2 emissions and play

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an important role in achieving carbon mitigation target. In 2007, these seven major sectors

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accounted for about 82% of total CO2 emissions, i.e., transport (25%), natural gas (14%), residential

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(10%), power generation (9%), construction (8%), services (8%), and electronics (8%) sectors

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(Figure 2a). Under the BaU scenario, CO2 emissions from residential, services, and construction

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sectors will significantly increase, while those from natural gas, power generation, and electronics

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sectors are decreasing from 2007 to 2030. With carbon mitigation, CO2 emissions from most sectors

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are projected to be reduced by 53%-89% and their carbon intensity will decrease by 29%-88% in

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2030 compared with the BaU scenario. The residential sector will decrease by the largest amount

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(70.5 Mt) in 2030, followed by transport (59.3Mt), services (36.1 Mt), and construction sectors

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(19.5 Mt). Accordingly, the transport sector contributes the largest CO2 emissions under NDC

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scenario, followed by services and power generation sectors. Unlike other sectors, the carbon

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intensity of the power generation sector has a very limited decrease of 1% in 2030 under the NDC

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scenario. The reason is that the power generation sector has high reduction rate of autonomous

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carbon intensity since this sector can cut emissions through the adoption of renewable energy

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resources and efficient technologies even without the implementation of carbon mitigation

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strategies. For example, the nuclear power accounts for 47% of the total energy source in the power

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generation sector in 2015 and the non-fossil power contributes an increasingly higher proportion

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of the total power generation. Even without the implementation of carbon mitigation, its carbon

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intensity decreases by 58% in 2030 relative to the 2007 level under the BaU scenario.

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Figure 2. Variations in the (a) sectoral CO2 emissions and relative CO2 intensity, and (b) primary

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primary energy use and relative energy intensity under different scenarios.

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3.2. Impacts on macroeconomics and industrial outputs

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The implementation of carbon mitigation has macroeconomic impacts on Shenzhen with all

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selected indicators decreasing since carbon emission is not a free product any more under the NDC

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scenario (Figure 3a). Among these macroeconomic indicators, the export and import will be

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influenced the most, with a loss of 3.3% and 3.6% in 2020 and 11.2% and 8.8% in 2030,

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respectively. The consumption is projected to be reduced by 2.0% in 2020 and 5.6% in 2030. The

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GDP and government expenditure will be slightly affected by a reduction rate of 0.1%-1.6% from

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2020 to 2030. For example, the GDP loss of Shenzhen is projected to be 0.2% in 2020 and 1.6% in

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2030, equivalent to 0.6 and 8.0 billion USD loss relative to the BaU scenario. The macroeconomic

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costs here were compared with the study in Shanghai,4 another pilot megacity to perform China’s

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NDC. To achieve the same carbon mitigation targets, the GDP and welfare losses of Shenzhen are

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slightly lower than in Shanghai. In contrast, Shenzhen suffers more export loss than Shanghai. The

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reason is that the main export products of Shenzhen are labor-intensive products such as textiles,

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clothes, and toys. Those labor-intensive sectors suffer larger economic output loss with the carbon

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mitigation constraints. As shown in Figure 3b, the sectoral outputs of some labor-intensive sectors

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will be significantly affected, e.g., the manufactured gas sector will suffer the most loss (90%) in

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2030, followed by natural gas mining (68%), transport (41%), non-metal (30%), and agriculture

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(12%) sectors. Other labor-intensive industries such as food production, textile, services, and

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construction sector also suffer noticeable output losses, ranging from 1% to 5%. To achieve the

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carbon mitigation target, industrial sectors with higher carbon and energy intensity rely on reducing

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productions and therefore would suffer economic loss. The projected total economic outputs loss

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is larger in 2030 (4%) than in 2020 (1%) due to the higher carbon intensity mitigation rate. The

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transport sector is the most influenced one and contributes to 64% of the total outputs loss. This is

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because, in the current model settings, transport sector still relies on traditional gasoline combustion

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engine which is vulnerable to the carbon cost. Shenzhen is home to the most famous electric vehicle 11 ACS Paragon Plus Environment

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manufacturer - Biyadi. Therefore, to further reduce the carbon intensity in the transport sector, the

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Shenzhen government should promote the use of new energy automobiles, such as hybrid and

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electric vehicles.

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Carbon mitigation is beneficial to certain industries, e.g., the outputs of power generation

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(6%), electronics (6%), and paper (4%) sectors will have a slight increase in 2030. In addition to

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the higher reduction rate of autonomous carbon intensity in the power generation sector, carbon

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mitigation decreases the proportion of direct use of fossil fuel and increases the electrification rate

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in the whole economy. As a result, the production of the power generation sector will be expanded.

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However, in regions with high shares of coal-fired thermal power and limited capacity of renewable

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energy, power generation sector may suffer an economic loss under carbon mitigation, e.g., the

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study in Shanghai.4 Electronics and paper sectors are easier than other sectors to reduce the carbon

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intensity by adopting advanced technology and renewable energy resources, which is represented

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in the CGE model by the substitution of energy by capital. Meanwhile, their production efficiencies

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will be improved due to the lower carbon transformation.

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Figure 3. Relative changes in (a) the selected macroeconomic indicators (b) the sectoral outputs

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(using a Logarithmic scale) between NDC and BaU scenario in 2020 and 2030.

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3.3. Impacts on water withdrawals

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Carbon mitigation accelerates local industrial structure upgrading by restricting carbon and

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energy-intensive industries. Since many of those industries are also water-intensive, it could be

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observed that carbon mitigation has co-benefits on local industrial water withdrawals. Under the

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BaU scenario, the total water withdrawals of Shenzhen will increase from 1,192 Mt in 2007 to

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1,317 Mt in 2020, and to 1,252 Mt in 2030 (Figure 4a). Although the economic output in 2030 is

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nearly four times that in 2007, the total water withdrawals only increase by 5%. The main reason 12 ACS Paragon Plus Environment


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is that water intensity will be gradually reduced due to the water-saving technology improvement,

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e.g., the total WI reduces by 51% (2020) and 79% (2030) compared to the 2007 level under the

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BaU scenario (Figure 4a). Carbon mitigation can further reduce total water withdrawals by 1% in

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2020 and 4% in 2030, approximately 14 Mt and 47 Mt per year, respectively.

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As shown in Figure 4b, carbon mitigation has uneven impacts on sectoral water use. To

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reduce local water use, we would expect that industrial sectors with comparative higher water use

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intensity appear in the co-benefits quadrant instead of in the worse-off and trade-offs quadrants.

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The synergistic water-saving effect is observed in most industrial sectors, e.g., services, transport,

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natural gas mining, non-metal, agriculture, food production, and textile sectors, and such effect is

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enhanced in 2030 compared to 2020 with more stringent carbon constraints. Compared to the

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sectors in the trade-offs quadrant, these sectors have higher WI, e.g., the mean water intensities

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(2011-2014) of natural gas mining, non-metal, agriculture, food production, and textile sectors were

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4, 4, 335, 3, 16 times of electronics sector (Figure S1 and Table S3). The decreasing proportion of

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these water-intensive industries will improve total water use efficiency, i.e., total WI decrease by

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less than 1% in 2030 compared with the BaU scenario. The limited decrease is because of the

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expansion of the services sector, e.g., under the BaU scenario, its proportion in total output is

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projected to increase from 24% in 2007 to 67% in 2030; in the same periods, the share of water

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withdrawals from the services sector increases from 25% to 70% (Figure 4a). In addition, WI in

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the services sector is close to the total WI, e.g., its WI is 5% higher than the total WI in 2030.

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Accordingly, the diminishing of these water-intensive industries has limited effects on the reduction

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of total water intensity. Services and transport sectors are the top two contributors to the reduction

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of both carbon emission and water use (Figure 4b), and it is estimated that there is a high potential

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to reduce water use by improving water use efficiency in the services and transport sector, e.g., a

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10% reduction of industrial water use is expected in 2020 compared with the level in 2010 with the

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use of water-saving devices in the these two sectors.5


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The carbon-water conflict is found in certain industrial sectors such as power generation,

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paper, and electronics sector. Carbon mitigation promotes the production in these sectors. Thus

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more attention should be paid to reduce water use intensity in these sectors, especially the power

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generation sector in the worse-offs quadrant, which has higher water intensity (Figure 4b, Table

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S3). Under the NDC scenario, water withdrawals from the power generation sector are projected

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to be enhanced by 6% because of its production expansion. Such trade-offs in water use may be

330

exacerbated if high water use intensity technologies are used to reduce carbon emissions in the

331

power generation sector. Water use intensity of the power generation is dependent on the choices

332

of the cooling system and renewable energy penetration,15, 16, 34 e.g., carbon mitigation may promote

333

the adoption of high water use intensity technologies such as nuclear and coal-fired plants with

334

carbon capture and storage (CCS) technology and thus increase total water use in the power

335

generation sector,12,14 although large-scale deployment of CCS technology is not possible in

336

Shenzhen by 2030. In the study area, increasing the proportion of nuclear power may have co-

337

benefits in water use since seawater is used for cooling in nuclear power generation. A study in

338

China’s nuclear power plants showed that the freshwater use in plants equipped with seawater

339

closed-loop cooling technology is only 2% of traditional nuclear power plants.35

340 341 342 343

Figure 4 (a) Variations in the annual sectoral water withdrawals and relative water intensity under

344

different scenarios; (b) The carbon emission change (NDC compared with the BaU scenario) versus

345

water use change (NDC compared with the BaU scenario) in 2020 and 2030(Bubble size indicates

346

sectoral water use intensity. In quadrant 4 (grey area), sectors have no water use and energy use

347

change because of relatively small outputs).

348 349 14 ACS Paragon Plus Environment


350

3.4. Impacts on water pollutants discharge.

351

As shown in Figure 5, carbon mitigation can reduce CODCr, NH3-N, petroleum, and V-ArOH

352

discharges, but slightly increase the discharges of heavy metal(loid)s, including Pb, Hg, Cd, and

353

As. In 2030, the CODCr, NH3-N, petroleum, and V-ArOH discharges under the BaU scenario are

354

estimated to be 1.9×104 t, 1.7×103 t, 10.1 t, and 0.8 kg, respectively. Under the NDC scenario, the

355

two main pollutants discharges will decrease by 2.2-2.4%, namely 431 t (CODCr) and 41 t (NH3-

356

N). The decrease of these two pollutants is also attributed to the local industrial structure upgrading

357

under the NDC scenario. Many of the carbon and energy-intensive industries have higher pollutants

358

discharge intensity, e.g., the mean CODCr and NH3-N discharge intensity (2011-2014) of non-

359

metal, food production, and textile sectors were 3, 4, 8 and 3, 4, 7 times the electronics sector (Table

360

S5), respectively.

361

Carbon mitigation has trade-offs on heavy metal(loid)s discharges, e.g., the Pb, Cr, Hg, Cd,

362

and As discharges will slightly increase by 0.43, -0.02, 0.03, 0.5, and 1.9 kg in 2030, respectively,

363

corresponding to increase rates of 2.0%, -0.01%, 4.3%, 3.8 %, and 4.6% of the BaU scenario.

364

Unlike CODCr and NH3-N, heavy metal(loid)s discharges are dominated by particular industries.

365

For example, under the BaU scenario the metal sector discharges 61% of V-ArOH, 53% of Pb, and

366

94% of Cr; the electronic sector discharges 46% of Pb, 38% of Hg, 81% of Cd, and 43% of As; the

367

power generation sector discharges 41% of Hg. Accordingly, these pollutants discharges are

368

sensitive to the implementation of carbon mitigation strategies. As mentioned in section 3.2, carbon

369

mitigation promotes the productions in electronics and power generation sectors and therefore

370

increase the discharges of Pb, Cd, As, and Hg. The increased discharges of heavy metal(loid)s have

371

limited impacts on the local water environment. The contributions of heavy metal(loid)s on the

372

Water Pollution Index (WPI) of Shenzhen River was as high as 5% during the period of 1984-1997,

373

but the heavy metal(loid)s discharge have been well controlled and their effects on water

374

environment became negligible since 1997.22

375

discharges will be decreased in 2030 relative to the 2007 level under both scenarios, e.g., Pb, Hg,

Our results show that the heavy metal(loid)s


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376

Cd, and As discharges are projected to decrease by 49%, 97%, 56%, and 63% in 2030 under BaU

377

scenario, respectively.

378

It should also be noted that the technology-induced reduction in pollutants discharge is larger

379

than the reduction induced by industrial structure change. As shown in Figure 5, the textile and

380

food production sectors are the top two contributors to CODCr and NH3-N discharges, accounting

381

for about 27% and 28% (CODCr), and 24% and 30% (NH3-N) in 2020 under the BaU scenario,

382

respectively. The pollutant intensity reduces much faster in the textile sector than in the food

383

production sector due to the wastewater treatment technology improvement (Table S6). With the

384

similar increase rates of economic outputs during the period of 2020-2030, the textile sector reduces

385

by 84% of pollutant emissions, but food production sector increases by 43% and becomes the top

386

emitters of CODCr and NH3-N in 2030 under both scenarios. Although carbon mitigation can reduce

387

pollutants discharge in the food production sector by 5%, more effort should be made to improve

388

cleaner technology.

389 390

Figure 5. Variations in the annual sectoral pollutants loadings under different scenarios.

391

392

4. DISCUSSION

393

4.1. Sensitivity Analysis

394

Given that the long-term projections may involve considerable uncertainties, we analyzed

395

several additional scenarios to gain a better understanding of how the water use and pollutants

396

discharge may potentially change with a range of different key factors. The uncertainties include

397

(1) the assumption of the future economic growth in the CGE model and (2) the calculation of the

398

future water withdrawals and pollutants discharge. The first uncertainty was assessed by adding

399

two GDP growth rates scenarios in the CGE model, i.e., the annual average GDP over 2007-2030 16 ACS Paragon Plus Environment


400

was increased from 7.9% to 9.1% or slowed down to 6.7%, corresponding to the 28% higher or

401

22% lower GDP in 2030 than that in the BaU scenario. The energy use, CO2 emission, GDP loss,

402

water demand, and pollutants discharge in 2030 under the GDP_high and GDP_low scenarios are

403

compared with the values in the NDC scenario, and their relative change rates are shown in Table

404

2. The GDP and welfare losses are less sensitive to GDP growth rate changes than the energy use,

405

CO2 emission, water demand, and water pollutants discharge. In addition, the impacts of the GDP

406

growth rate changes on the energy use, CO2 emission, and water use are slightly larger compared

407

with most of the water pollutants discharges except for Hg. Regarding the secondary uncertainty,

408

we calculated the 95% CI of WR and PR in each sector, and their impacts on total water use and

409

pollutant discharges are also shown in Table 2. Water demand ranges between -17.0% and 29.7%,

410

CODCr and NH3-N discharges range between -5.0% and 29.8%, petroleum discharge between -8.5%

411

and 9.6%, Pb, Cr, Cd, and As between -0.8% and 3.6, and V-ArOH and Pb between -41.6% and

412

190.4%. This indicates that V-ArOH and Pb discharge are very sensitive to technology

413

improvement in wastewater treatment. Nevertheless, the projected V-ArOH and Pb discharge are

414

only 0.56 and 0.42 kg under NDC scenario in 2030 so that the large variability of these two

415

pollutants are not likely to have a significant influence on the results.

416 417 418

Table 2. Relative Changes from the projected values in 2030 under the NDC scenario

419 420

4.2. Policy implications

421

Currently, the energy-water nexus has been given scant consideration in the energy and water

422

management in China. One possible reason is that policy-making related to energy and water are

423

performed by different departments without fully understanding of the other’s needs. The integrated

424

model developed in this study captures the cross-sector interactions and feedbacks among

425

economic, energy, and water systems, and thus it can be used to help policymaker identifying the 17 ACS Paragon Plus Environment

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426

possible co-benefits or trade-offs across these systems and designing effective policies and

427

measures. The suggested strategies include: (1) mitigating the energy-water conflict. Carbon

428

mitigation promotes the electrification rate in the whole economy and expands the production in

429

the power generation sector in Shenzhen. Due to its relatively higher water use intensity than other

430

sectors, trade-offs in water use in the power generation sector are observed. In addition, carbon

431

mitigation at national scale can increase the shares of low-carbon energy in power generation in

432

China. This will probably increase the consumptive water use from power generation sector due to

433

the adoption of high water use intensity technologies such as nuclear and coal-fired plants with

434

carbon capture and storage (CCS) technology, especially in areas where freshwater is used for

435

cooling in power plants. However, power plants may prefer to save energy other than water under

436

the current examination system.35 Therefore, incentive measures such as allowances for water

437

saving and preferential taxes for power plants equipped with low water intensity technology should

438

be promoted to improve water use efficiency in power generation. In addition, to reduce the

439

negative impacts of carbon mitigation on the heavy metal(loid)s discharges, the local government

440

should close small-size plants which cannot satisfy the national discharge standards, particularly in

441

sectors such as chemical, metal produces, electronic, and power generation. Also, cleaner

442

technologies should be promoted in both existing and new plants by setting pollutant discharge

443

standards; (2) Promoting the energy-water co-benefits in the transport and services sectors. They

444

are the top two contributors to the reduction of both CO2 emissions and water use. It should be

445

noted that to fulfill the carbon mitigation target, the transport sector is projected to suffer significant

446

economic loss, e.g., its economic output will be reduced by nearly 41% in 2030 compared with the

447

BaU scenario. To reduce these economic losses, low-carbon transport policies including speed

448

control, an intelligent transport system, and mass transit systems improvement are suggested.36

449

These two sectors also have lower levels of water-related pollutant emissions and high potential to

450

reduce water use intensity, which implies that the local government should pay more attention to

451

the utilization of water-saving technologies in these sectors. 18 ACS Paragon Plus Environment


452

Based on the analysis, more coordinated policies need to be designed to ensure water and

453

energy security in the future, especially in the light of governmental reform that the carbon

454

mitigation responsibility has been moved into the newly founded Ministry of Ecology and

455

Environment in 2018. The study highlights the importance of integrated approaches in energy and

456

water resources management. Although the current study focuses on a populated, developed

457

megacity in southeast China, the framework could be applied at the city, region, and national scales,

458

especially in areas with severe competition for energy and water resources.

459 460

4.3. Limitations

461

The effects of carbon mitigation on pollutants discharge in construction, transport, and

462

services sectors are not considered due to the lack of data in this study. In China, the water

463

pollutants discharges from these three sectors are assumed to be related to residential activities and

464

not reported separately. Considering the projected economic output decrease in these sectors with

465

carbon mitigation, the co-benefits on the water pollutants discharge may be underestimated. In

466

addition, with carbon constraints, some labor-intensive sectors, such as natural gas mining, non-

467

metal, agriculture, food production, textile, service, and construction sectors, suffer more economic

468

output losses. It implies that the future local labor force market would be significantly influenced,

469

and the total labor force demand is expected to decrease. Labor force migration accounts for nearly

470

70% of the total population in Shenzhen in 2016. Therefore, domestic water use and pollutants

471

discharge are expected to be greatly reduced. We did not consider this co-benefit in our study due

472

to the lack of detailed sectoral labor force data.

473


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474 475 476 477 478 479 480 481 482 483

Supporting Information. Table S1, selected indicators of Shenzhen and their percentages in China in 2007; Table S2, sector classification of the CGE model; Table S3, average water intensity (2011-2014) in different sectors; Table S4, estimated parameter values for water use intensity; Table S5, average pollutant discharge intensity (2011-2014) in different sectors; Table S6, estimated parameter values for pollutant intensity; Figure S1, the carbon emission change rate (NDC compared with the BaU scenario) versus water use change rate (NDC compared with the BaU scenario); Equations and parameters in the CGE model; Figure S2, production tree of basic sectors; Figure S3, production tree of energy transformation sectors.

484

Acknowledgment

485

The authors would like to thank Dr. Lei, Liu (Associated professor at the School of Public

486

Administration of Sichuan University, China) for assistance in collecting data of Input and output

487

Table and Energy Balance Table of Shenzhen. This research was supported by the National Natural

488

Science Foundation of China (51861135102,71704005, 71690241, 71690245), the special fund of

489

State Key Joint Laboratory of Environment Simulation and Pollution Control (18K01ESPCP), and

490

the Key Projects of National Key Research and Development Program of the Ministry of Science

491

and Technology of China (2017YFC0213000).

492 493 494 495 496 497

498

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499 500 501 502 503 504 505 506

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