Sectoral energy-carbon nexus and low-carbon policy alternatives: A case study of Ningbo, China

Journal of Cleaner Production 156 (2017) 480e490

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Sectoral energy-carbon nexus and low-carbon policy alternatives: A case study of Ningbo, China Dewei Yang a, c, *, Bin Liu a, b, Weijing Ma a, b, Qinghai Guo a, Fei Li d, Dexiang Yang e a

Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China University of Chinese Academy of Sciences, Beijing 100049, China c Ningbo Urban Environment Observation and Research Station-NUEORS, Chinese Academy of Sciences, Ningbo 315800, China d Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China e School of Forestry, Northeast Forestry University, Harbin 150040, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 February 2017 Received in revised form 11 April 2017 Accepted 11 April 2017 Available online 13 April 2017

The intensive energy consumption in urban sectors is aggravating global warming, which triggers an indepth thinking about energy-carbon nexus and low-carbon city actions. Hence, China has launched a pilot low-carbon city program to explore low-carbon pathways since 2010. This study employed a Longrange Energy Alternatives Planning System model (LEAP) to simulate six energy sectors-related GHG emissions in a pilot low-carbon Ningbo city, China. The LEAP-Ningbo model comprises three basic modules, i.e. energy supply, energy transformation and end-use energy demand in six urban sectors (i.e., household, service, agriculture, transport, industry, and transformation sectors), and resulting environmental impacts (CO2 equivalents). The results identified by the business as usual (BAU) scenario indicate that total energy consumption is expected to reach 449.72 Mtce and results in emissions of 651.83 Mt CO2e by 2050. In contrast, more aggressive policies and strategies involved in the integrated scenario (INT), which combines the energy structure optimization (ESO) scenario with the policy-oriented energy saving (PES) scenario, can lower energy demand by 14% and CO2e emissions by 27%. A comparison among global cities and low-carbon plans helps identify the carbon emission level and define the actionable low-carbon policies. The high correlation between sectoral energy use and resulting GHG emissions implies energy-carbon reduction efforts, e.g., low-carbon energy substitution, intensive energy-saving policies, the improvement of energy efficiency, and industrial transformation. Achieving low-carbon city targets requires timeline-restricted actions and backgrounds-based measures in plans. The results of this study shed light on if and how cities can shape energy-carbon reduction trajectories and develop low-carbon pathways in China and beyond. © 2017 Elsevier Ltd. All rights reserved.

Keywords: GHG emissions Energy saving LEAP Low-carbon city Scenarios

1. Introduction 1.1. Motivation World-wide actions have been taken to curb global warming. The Intergovernmental Panel on Climate Change (IPCC) reported that it is more than 95% certain that global warming is mostly being caused by anthropogenic activities (IPCC, 2014). The climate models

* Corresponding author. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China. E-mail addresses: [email protected], [email protected] (D. Yang). http://dx.doi.org/10.1016/j.jclepro.2017.04.068 0959-6526/© 2017 Elsevier Ltd. All rights reserved.

indicate that during the 21st century the global surface temperature is likely to rise a further 2.6
Ce4.8
C for the IPCCs highest emissions scenario using stringent mitigation (IPCC, 2014). Subsequently, in 2015, after the tough political negotiations, 195 countries agreed by consensus at the Paris Climate Summit to set a goal of limiting the temperature increase to 1.5
C. Prior to this, global warming caused by Greenhouse Gas (GHG) emissions has attracted academic interest for long period (Grimm et al., 2008; Wang et al., 2011; Yang et al., 2013; 2016a). Among diverse anthropogenic activities, the energy consumption of urban sectors is the major contribution to GHG emissions, globally (Moomaw, 1996). Global energy-related carbon emissions accounted for 65% of the total GHG emissions in 2010, of which 55% were from industry (IEA, 2014), owing to rapid economic growth, urbanization and population expansion. The world is undergoing

D. Yang et al. / Journal of Cleaner Production 156 (2017) 480e490 Nomenclatures N1. Abbreviation 1) CO2 equivalents, CO2e 2) Gross Domestic Production, GDP, billion yuan 3) Greenhouse Gas, GHG, CO2, N2O, and CH4 in this study 4) Intergovernmental Panel on Climate Change, IPCC 5) Long-range Energy Alternatives Planning System model, LEAP 6) Million ton coal equivalent, Mtce 7) Million ton CO2 equivalents, Mt CO2e N2. Scenarios 8) Four scenarios in the LEAP-Ningbo model: Business As Usual scenario, BAU; Energy Structure Optimization, ESO; Policy-oriented Energy Saving, PES; Integrated scenario, INT 9) Six sub-scenarios in the LEAP-Ningbo model: Clean Energy Substitution, CES; Combined Heat and Power generation, CHP; Industry Energy Saving, IES; Power-Saving Appliance, PSA; Renewable and new Energy Utilization, REU; Transport Energy Saving, TES 10) Six energy-related sectors: Household, H; Transport, Tp; Industry, I; Agriculture, A; Service, S; Transformation, Tf

unprecedented rapid urbanization while consuming large amount of energy in many urban sectors. Urban areas, which occupy 2.4% of the global land surface, are estimated to consume 65% of the energy and produce up to 80% of the carbon dioxide (Grimm et al., 2008; Churkina, 2008). In 1900, a mere 10% of the global population was urban dwellers. Not surprisingly, that ratio will exceed 60% by 2030 (Grimm et al., 2008). In China, the urbanization rate reached a record 56.1% in 2015 from 17.9% of 1978 (NBS, 2016). In the next 15 years, the annual migration towards cities is expected to be approximately 20 million people from rural China. As a result of such urbanization, China accounted for 28% of global total CO2 emissions in 2013 (IEA, 2014). Cities are critical because they concentrate socio-economic activities that produce climate change related emissions, and are therefore responsible for GHG mitigation. Hence, it is indispensable to trace the characteristics and estimate the trajectories of energy-related GHG emissions at the urban level, which can help form effective low-carbon strategies and ultimately achieve overall national targets. Low-carbon actions and practices have prevailed along with aggravating global warming. In 2009, China committed to reducing the carbon intensity of its Gross Domestic Production (GDP) by 40e45% by 2020 compared to the 2005 baseline (Wang et al., 2011). In 2010 and 2012, the National Development and Reform Commission of China announced the selection of pilot provinces and cities to explore the low carbon development work. These pilot low carbon cases were selected based on geographic, social and economic diversity and representativeness, existing foundational and/ or preparatory work in low carbon development and demonstrated interest by the local regions to be a pilot location (NDRC, 2010; Khanna et al., 2014). This triggered some in-depth thinking about where a lowcarbon city will fit specific targets. It is inevitable for us to help facilitate feasible city sector-related measures for seeking a low carbon future. Some studies have already discussed China's lowcarbon city initiatives, e.g., the effectiveness of low-carbon city measures (Lin et al., 2010), low-carbon towns/city in China (Li et al., 2012; Yang and Li, 2013), Low-to-no carbon city (Lehmann, 2013), China's low-carbon city initiatives (Lo, 2014), and Low-carbon city logistics distribution network (Yang et al., 2016b), but most of them are only based on urban GHG emissions. Several studies have discussed the carbon-related issues in Ningbo (Wu et al., 2015; Mi et al., 2016). Meanwhile, low carbon actions combined with sectoral reduction strategies for energy-related GHG emissions need to be fully investigated for establishing model practices for Chinese cities.

481

1.2. Literature review An increasing amount of literature has paid attention to the exploration of low-carbon development pathways and potential abatement strategies. One research stream focuses on tracing the intrinsic relationships between energy-related GHG emissions and their contributors. These previous studies include the Stochastic Impact by Regression on Population, Affluence, and Technology (STIRPAT) method (Wang et al., 2013), the Logarithmic Mean Divisia Index (LMDI) method (Cansino et al., 2015), the Impact ¼ Population$Affluence$Technology (IPAT) method (Dietz and Rosa, 1997), the Kaya Index method (Mavromatidis et al., 2016), the input-output structural decomposition analysis (IOSDA) method (Chen et al., 2016; Wei et al., 2016), etc. These methods attempt to seek solutions for decoupling economic growth and energy intensity, and explore the optimal means of reducing GHG emissions. However, a simple GHG emissions index conveys limited information of sectoral disparities and technoeconomic connection on GHG emissions. Most studies have only analyzed the emissions driving forces at the national level; few studies have focused on city-level emissions (Kang et al., 2016; Yu et al., 2015). The other research stream employs comprehensive energyeconomic models (e.g., top-down, bottom-up and hybrid types) in quantifying the sectoral or regional energy-related GHG emissions, and simulating future dynamics. The top-down models, e.g., The Computable General Equilibrium (CGE) model (Naqvi and Peter, 1996), examines the relationships among energy activities and economic indices on a macro level, which does not depict technological details of energy production and consumption (Wen et al., 2014). The typical bottom-up models, e.g., the market allocation (MARKAL) model (Kannan, 2011), the model for evaluating regional and global effects of GHG reduction policies (MERGE) (Manne et al., 1995), the systems-engineering optimization MESSAGE model (Messner and Schrattenholzer, 2000), the computable general €fer equilibrium and market allocation (CGE-MARKAL) model (Scha and Jacoby, 2006), the Long-range Energy Alternatives Planning system (LEAP) (Lin et al., 2010), and Asian-Pacific Integrated Model (AIM) (Wen et al., 2014), can describe production-related technologies and predict future trends by way of energy consumption and production. These models and their combination enable a macro evaluation of energy use-induced environmental effects. Overall, evaluating energy-driven GHG emissions using systematic models is becoming the preferred method. However, some methods cannot be applied for the development of city-scale carbon emissions benchmarks and mitigation strategies with timelines and inventories. Recent years have shown that the LEAP model is widely adopted to extract energy policies at the city, national, and global scale (Emodi et al., 2017). The LEAP model was used to explore the energy, environmental and economic in€ fluences of consumption activities in the electricity sector (Ozer et al., 2013; McPherson and Karney, 2014), transportation sector (Sadri et al., 2014; Shabbir and Ahmad, 2010), the iron and steel sector (Ates, 2015), energy sector (Huang et al., 2011) and policy interventions (Phdungsilp, 2010). Compared to top-down and other hybrid models, the bottom-up LEAP model has a flexible data structure which is not only easy to use, but also rich in technical and end-user details (Emodi et al., 2017). It can deliver energy scenariobased GHG emissions accounting combined with a set of carbonreduction policies. However, the existing literature delivers very limited analysis of reducing GHG emissions and updating energy use structures for addressing the gap of low-carbon city targets, and facing Chinese cities seeking suitable low-carbon pathways.

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1.3. Contribution In this paper, a LEAP-Ningbo model was first proposed to estimate six sectors-based energy consumption and resulting CO2e emissions in Ningbo. This bottom-up model could fill knowledge gaps between low-carbon actions and various urban backgrounds for seeking suitable pathways. The main contribution of this paper is threefold: 1) Modeling the dynamics of six sector-based energy consumption and GHG emissions using the LEAP-Ningbo model, and identifying reduction potential for energy consumption and GHG emissions; 2) Discussing sectoral energy-carbon nexus and long-range alternative policies and measures for energy saving and emission mitigation in Ningbo. 3) A comparison among global cities and low-carbon plans helps locate the carbon emission level and define the actionable lowcarbon policies.

1.4. Organization of the paper The rest of the paper is organized as follows: Section 2 elaborates the LEAP-Ningbo model, including basic model description, scenario design, calculation methods, and sensitive analysis. Section 3 introduces case study, related parameter and data resources for the proposed model. The results and discussion are presented in Section 4 and Section 5. Section 6 concludes this paper. 2. LEAP-Ningbo model 2.1. Basic model description The LEAP model was developed by the Stockholm Environment

Institute (2011). It is a bottom-up simulation model, widely used to track energy demand and supply, which reports the system environmental impacts and provides alternative policies for GHG mitigation. It allows accounting for both energy sector and nonenergy sector GHG emissions sources and sinks (Huang et al., 2011). In order to profile the sector-based potential for energy-saving and GHG emissions reduction in Ningbo, China, we establish a LEAP-Ningbo model. The analytical framework based on the LEAP model is depicted in Fig. 1. The model comprises three basic modules, i.e. energy supply, energy transformation and end-use energy demand in urban sectors (i.e., household, service, agriculture, transport, and industry sectors in this study), and resulting environmental impacts (CO2 equivalents, i.e., CO2, N2O, and CH4 in this study). The model branches of the LEAP-Ningbo model are shown in Fig. S1. The energy supply refers to the input of primary and secondary energy resources on the supply-side, i.e., natural gas, coal, fuel oil, kerosene, diesel, gasoline, electricity, compressed natural gas, liquefied petroleum gas, heat, and clean energy. The transformation sector is comprised of conversion of primary energy to secondary energy and further transformation. The transformation sector includes electricity generation, petroleum refining, and coal refining. Further transformation includes electricity transmission and distribution centers, which deliver power to the final consumers (enduse demand). Electricity generation consists of thermal, hydro, liquefied natural gas combustion, solar, wind and garbage incineration. The end-use energy demand is also classified into the household, transport, industry, agriculture, and service sector. Each sector includes corresponding branches, energy-using devices, and fuel types. The household is further divided into urban and rural households. Various household appliances represent the differences in urban and rural households. The transport sector is made up of five categories which include road, railway, waterway, air, and slow-speed transport. The agriculture sector encompasses farming, husbandry, fishing, and forestry sector. The service sector refers to

Fig. 1. Research flowchart based on the LEAP-Ningbo model.

D. Yang et al. / Journal of Cleaner Production 156 (2017) 480e490

Tf

the tertiary industry sector except transport.

483

2.2. Scenario design and calculation methods

PSA

TES

IES PES

CHP

REU

CES

Notes: 1) H, Household; Tp, Transport; I, Industry; A, Agriculture; S, Service; Tf, Transformation. 2) The shaded grid indicates that the sector takes such policies and measures.

H To do nothing to influence urban energy demand By 2020, the proportion of natural gas will reach 95% in the central city The proportion of liquefied petroleum gas in the central city is 5% by 2020 The proportion of clean energy increases 5 times by 2030 The proportion of wind, hydro energy, biomass and solar energy reaches 3.2 billion kWh in 2020 Energy efficiency improvement; proportional increase of liquefied natural gas; proportional reduction of coal by 5% by 2030 Eliminating outdated production capacity; adjusting the industrial structure; improving energy efficiency by 2% Promoting public transport; increase in buses; encouraging taking public transport with an increase of 30% in 2020 Improving energy efficiency and promoting ‘oil to gas’; proportional increase in liquefied petroleum gas and compressed natural gas-fueled taxis and buses; the proportion of natural gas will reach 90% in buses by 2020 Promoting energy-saving appliances in residential, commercial, and public buildings; energy-saving appliances used in 80% of households by 2030 ESO

where, SAF is the sensitivity coefficient of the result R to the influencing factor F, △F is the change rate of the uncertainty factor, △R is the corresponding change rate of result R when the change of △F happens. When SAF >0, the result with the uncertainty factor changes in the same direction; When SAF <0, the result with the uncertainty factor changes in the opposite direction. The larger absolute value of the SAF means the result is the more sensitive to the uncertainty factor. In another word, the uncertainty factor influences more on the result.

BAU INT

SAF ¼ DR=DF

Sub-scenarios

Given multiple data sources for energy consumption and GHG emissions has been shown in Section 3.3, the single factor sensitivity analysis was performed to investigate the impact on the result caused by key parameters in the scenarios setting. The single factor sensitive analysis allows us to estimate the sensitivity of the result (i.e., GHG emissions) caused by the change of each factor and the resulting sensitivity coefficient. The formula is as follows:

Table 1 Policy options and assumptions in six sectors-based scenarios.

2.3. Sensitivity analysis

Scenarios

Policies and measures

Sectors

Tp

I

A

S

The scenario analysis allows us to learn the trends in energy allocation and CO2e changes that are influenced by a particular situation, low-carbon targets in this study, over a predicted time. In order to assess the low-carbon effects of energy savings and emissions mitigation in Ningbo, the study used the business as usual (BAU) scenario as a benchmark for the development of three policy scenarios, which are Energy Structure Optimization (ESO), Policy-oriented Energy Saving (PES) and integrated (INT) scenarios. The BAU scenario assumes the current situation of energy use and CO2e emissions without any low-carbon-oriented policy interference in Ningbo. In this scenario, new techniques are not introduced to lower the energy intensity and CO2e emissions. The main aim of this scenario is to highlight the existing situation and what it looks like with routine policies. The ESO scenario designs strategic policies from the perspective of energy structural adjustment. The PES scenario adopts feasible energy saving policies. The INT scenario combines ESO with PES scenarios, and encompasses six sets of reduction measures, i.e., clean energy substitution (CES), renewable and new energy utilization (REU), combined heat and power generation (CHP), industrial energy saving (IES), transportation energy saving (TES), and power-saving appliances (PSA). The INT scenario includes all energy-saving measures in local plans and policies adopted by other low-carbon practices. The sector-based policy options and scenarios assumption are displayed in Table 1. The key baseline variables are packed in the LEAP-Ningbo model (listed in Table 2). The LEAP-Ningbo model was developed to forecast the energy demand, CO2e emissions and reduction potential under the four designed scenarios. The study period starts from 2012 to 2050, with 2012 as the baseline year. The model helps extract low-carbon energy measures and policies for city-scale actions. Total energy consumption and accompanying GHG emissions are both from energy demand and energy transformation, and are calculated for the predicted period in each scenario. The corresponding methods are embedded in the LEAP model, which are presented in the Supplementary Materials and mostly referenced from Lin et al. (2010).

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Table 2 Key baseline variables in the LEAP-Ningbo model. Parameters

2012a

2020

2025

2030

2035

2040

2045

2050

Population (million)b Population growth rate (%)c Family size (person)d Family numbers (million)e GDP (billion yuan)f GDP growth rate (%)g

7.64 0.81 2.47 3.09 658.22 8.40

8.05 0.57 2.47 3.26 1250.26 8.00

8.28 0.57 2.47 3.35 1837.04 7.75

8.52 0.50 2.47 3.45 2686.72 7.50

8.74 0.50 2.47 3.54 3857.14 7.25

8.96 0.50 2.47 3.63 5511.66 7.00

9.18 0.50 2.47 3.72 7730.39 6.75

9.42 0.50 2.47 3.81 10,791.61 6.50

Notes: a The key variables are from Ningbo statistical yearbook 2013 and the Sixth Census Bulletin for Ningbo. b The population used in this paper refers to residential population. c 0.81% is the average annual population growth rate during 2012e2015, 0.57% is the average annual population growth rate during 2010e2015, 0.50% is the annual population growth rate predicted by the General Plan of Ningbo (2004e2020). d The family size is set to 2.47 for simplicity. e The total local resident population divided by the family size is family numbers. f The GDP of Ningbo is current price. g The GDP growth rate for different time periods are assigned based on the 12th five-year development program for Ningbo and the General Plan of Ningbo (2004e2020).

3. Case study and data collection 3.1. Case study Ningbo, a sub-provincial city and international port, is located in coastal central China. It covers a land area of 9816 km2 and had a total population of 7.64 million in 2012. As a harbor city, Ningbo has experienced rapid development, with regional GDP of 658.22 billion yuan in 2012, up from 2.02 billion yuan in 1978. The proportion of three industry structures was 4.08: 53.43: 42.49 in 2012. As one of national petrochemical bases, Ningbo has huge volumes of energy consumption and resource output. In 2012, total end-use energy consumption reached 39.38 Mtce. To explore low-carbon pathways, Ningbo was chosen as one of the second pilot low carbon cities in 2012. The heavy industrial structure and high-carbon energy consumption make Ningbo city a typical case for illustrating GHG emissions reduction strategies and a low carbon roadmap for cities. 3.2. Baseline variables and parameters The key baseline variables packed in the LEAP-Ningbo model, such as population, population growth rate, family numbers, family size, GDP, and GDP growth rate, are listed in Table 2. In the study, we evaluate missions of the dominant GHGs (CO2, N2O, CH4) resulting from sector energy consumption. N2O and CH4 were converted into CO2 equivalents on the basis of their 100-year global warming potential, using the IPCC default coefficients of 34 and 298 in the Fifth Assessment Report of IPCC, respectively. The related GHG emissions factor used in the LEAP-Ningbo model could be found in Table S1. The major parameters used in sector-based scenarios in the LEAP-Ningbo model are given in Table S2.

survey. The tendency data were collected from local plans and predicted using the average development data. These plans include the 12th Five-year Plan of Ningbo City, the12th Five-year Transport Plan of Ningbo, the 12th Five-year Plan Environmental Protection of Ningbo, the General Plan of Ningbo (2004e2020), the Ningbo ecocity Plan, and the Implementation Plan of Pilot Low-carbon City in Ningbo. The parameters were collected from the Common Calculation Rules for Comprehensive Energy Consumption (GB/ Te2589e2008), the Fifth Assessment Report of the IPCC and embedded in the LEAP model. 4. Results The LEAP-Ningbo model, based on energy parameters and socio-economic development indicators, provides the trajectory and structure of six energy sectors-related energy consumption and CO2e emissions. This allows us to identify the reduction potential of energy consumption and CO2e emissions by measure and sector between scenarios. We also provide the sensitivity analysis of GHG emissions resulting from key parameters changes. Subsequently, to identify the level of carbon emissions and define the actionable low-carbon policies, a comparison was made among cities and lowcarbon plans. 4.1. Energy consumption The total energy consumption and its structure changes under the BAU, ESO, PES, and INT scenarios during 2012e2050 are shown in Fig. 2. Generally, energy consumption will increase steadily up to

3.3. Data collection Four kinds of data were collected for modeling processes as follows: historical data, current data in 2012, tendency data and reference parameters. The historical data were used for key assumptions and setting up baseline scenarios. These socio-economic data, e.g., population, GDP and industrial structure, were from the Ningbo statistical yearbook (NSY) 2006e2013, the sixth census bulletin for Ningbo, and the General Plan of Ningbo (2004e2020). The current data in 2012 refer to energy supply, transformation and demand in six sectors, which were collected from the Ningbo Statistical Yearbook 2013, the Ningbo Energy Report 2012, the 12th Five-year energy-saving Plan of Ningbo, the Special Gas Plan of Ningbo, the 12th 5-year Energy Saving Plan of Ningbo, and a local

Fig. 2. The total energy consumption under the four scenarios from 2012 to 2050.

D. Yang et al. / Journal of Cleaner Production 156 (2017) 480e490

2050 under each scenario, but with different growth rates. Under the BAU scenario, the total energy consumption will reach 449.72 Mtce by 2050 from 105.71 Mtce in the base year 2012, with an annual growth rate of 3.88%, the highest amongst the four scenarios. Given a series of energy reduction measures and fuel technology switching, the total energy consumption will be reduced to 385.78 Mtce in 2050 under the INT scenario, which will save 14.22% compared to the BAU scenario. As to the sectoral structure of energy consumption, much of the energy consumed in the BAU scenario comes from the transformation sector with a share of 62%, which is followed by industry (25%), transport (5%), service (4%), household (3%), and agriculture (1%) in the base year (see Fig. S2). The trend shifts apparently with an increase in the service sector, while there is a decrease in the transformation and transport sector by 2050. Due to the series of energy structural adjustments and energy policies introduced, the total energy consumption will be the lowest under the INT scenario. The transformation sector with an obvious decrease is expected to reach 37%, while the most increase is in the industry (35%) and service sector (25%) by 2050. The structural change of final energy usage from 2012 to 2050 is displayed in Fig. S3. In general, fuel oil, coal and electricity will still dominate the Ningbo energy consumption system by 2050 under the four scenarios, with greater proportional changes among fuel types. This indicates that future economic development in Ningbo will rely heavily on the above fuel types. Under the BAU scenario, fuel oil, coal and electricity in 2012 cover 40%, 31%, and 16% of total energy consumption, respectively. In contrast, in 2050, the proportion of fuel oil, coal, and electricity will change to 26%, 19% and 31% of total energy consumption under the INT scenario, respectively. In addition, the diesel, heat, natural gas, liquefied petroleum gas, clean fuel (i.e., wind, solar, hydro, geothermal, and biomass), and compressed natural gas increase differently, and gasoline decreases distinctly. These changes of energy structure are seen as a result of clean fuel introduction and energy resources diversification. The share of coal and fuel oil decreased from the BAU scenario (31%, 40%) to ESO (20%, 26%), PES (26%, 23%), and INT (20%, 26%), respectively, due to the reduction of pollution-related energy resources. Moreover, the share of clean energy usage, including electricity, clean fuel, liquefied petroleum gas, compressed natural gas, and natural gas will increase rapidly across ESO, PES, and INT scenarios, contributing about 40% by 2050. Therefore, the ESO, PES and INT scenarios promise us an environmentally friendly future with clean energy substitution and low-carbon energy technology innovation in Ningbo. 4.2. Greenhouse gas emissions The total energy use-related CO2e emissions under the four scenarios from 2012 to 2050 are demonstrated in Fig. 3. Under the BAU scenario, emissions will increase from 150.29 Mt CO2e in 2012 to 651.83 Mt CO2e in 2050 with an annual growth rate of 3.94%. GHG emissions increase to 475.68 Mt CO2e in 2050 with a significantly lower annual rate of increase of 3.08% under the INT scenario. Under the ESO and PES scenarios, emissions will increase to 589.17 Mt CO2e and 535.02 Mt CO2e in 2050 with a significant growth rate of 3.66% and 3.40%, respectively. Obviously, the proposed policies and measures under the INT scenario result in a reduction of 175.10 Mt CO2e emissions by 2050. It is observed that CO2e emissions ascend directly with the increase of energy consumption (compared with Fig. 2). The CO2e emissions in Ningbo steadily rise with local GDP's growth (compared with Table 2). Moreover, fuel oil consumption contributes most of the CO2e emissions, but gradually declines with

485

Fig. 3. The CO2e emissions during 2012e2050 under the four scenarios.

the rising share of CO2e emissions produced by electricity and clean fuel (compared to Fig. 2). The transformation sector contributes the most (over 50%) to the total emissions under the four scenarios before 2035 (see Fig. 4). The emissions in the service and industry sectors increase with the fastest annual growth rate of 9.62% and 5.74% under the BAU scenario, respectively. This is the same as the INT scenario with an annual growth rate of 9.61% in the service sector and 4.54% in the industry sector. 4.3. Reduction potential for energy use-driven greenhouse gas emissions The energy-saving potential and it's contributing measures and sectors under the INT scenario compared with the BAU scenario are reflected in Fig. 5. Given the implementation of all energy-saving policies and measures, the energy-saving potential in Ningbo will increase gradually from 4.66 Mtce in 2020 to 63.94 Mtce in 2050. Among energy-saving measures, the industrial energy saving measure contributes the largest share of energy reduction with over 70% by 2050, followed by the power-saving appliance measure, but the combined heat and power generation and clean energy substitution measures decline gradually. In terms of sectoral contribution, the industrial sector ranks in first place with over 70% by 2050 and increases annually, followed by the service sector with more than 10%. Other sectors perform with declining energy gradually. It is worth noting that the only decline in energy consumption results from the household sector. Fig. 6 displays the future potential for CO2e emissions reduction and the contributing share of each measure and sector. Results indicate that CO2e emissions reduction has a large potential along with less energy consumption when all energy-saving measures are taken, with 176.15 Mt CO2e by 2050 from 35.89 Mt CO2e in 2020. In regard to the contributing measures, the greatest contribution is associated with the industrial energy saving measure with more than 60% by 2050, followed by the renewable and new energy utilization measure (over 20%) and the clean energy substitution measure (more than 10%), but dropping by year. It is noted that the industrial energy saving measure will grow rapidly. In the sectoral contribution, the industry sector covers the highest share with over 60% of emissions by 2050, while the transformation sector gradually drops with a share of less than 30%. The other sectors contribute a small share except the transport sector (about 5%). 4.4. Sensitivity analysis The sensitivity analysis of GHG emissions resulting from key

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Fig. 4. Proportion of CO2e emissions by sector under the four scenarios.

Fig. 5. The energy-saving potential by measure (a) and sector (b) under the INT scenario compared with the BAU scenario.

Fig. 6. The reduction potential of CO2e emissions by measure (a) and sector (b) under the INT scenario compared with the BAU scenario.

parameters changes has shown in Table 3. It is obvious that the sensitivity of GHG emissions varies notably with various influencing factors. In these factors, GHG emissions is most sensitive to the growth rate of energy efficiency under the CHP, while is little sensitive to the proportion of natural gas under the CES. It's pointed out that GHG emissions do not change with the proportion of energy-saving appliances under PSA. The GHG emissions change with other influencing factors in the opposite direction except the population under the BAU.

4.5. Comparisons among cities and plans A comparison among cities allows us to identify the level of carbon emissions and the actionable low-carbon policies. A comparison of per capita carbon emissions among global cities is shown in Fig. 7. It indicates that Ningbo city displayed a higher level of per capita carbon emissions. Overall, the per capita carbon emissions in mainland China's cities was much lower than USA cities, but higher than European cities and comparable to other Asian cities. This

D. Yang et al. / Journal of Cleaner Production 156 (2017) 480e490

487

Table 3 The sensitivity analysis of GHG emissions. Key parameters tested in scenario setting

Population under BAU Proportion of natural gas under CES Amount of renewable energy under REU Growth rate of energy efficiency under CHP Growth rate of energy efficiency under IES Growth of proportion of public transport under TES Growth rate of energy efficiency under TES

Sensitivity of GHG emissions under the INT scenario 2020

2030

2050

0.0124 0.0005 0.1197 0.1788 0.0101 0.0085 0.0153

0.0078 0.0008 0.1258 0.4347 0.0226 0.0072 0.0171

0.0059 0.0013 0.0854 0.2950 0.0584 0.0048 0.0141

Fig. 7. Per capita carbon emissions among global cities (Measuring standards: CO2 in Ningbo, C in Beijing, and CO2e in Xiamen and Nanjing. Data from refs (Shabbir and Ahmad, 2010; Phdungsilp, 2010)).

performance may be associated with local industrial levels and energy structure. Identifying sector-related measures adopted by low-carbon plans could help shed light on how different local governments perceive and define the gaps and paths toward low-carbon targets. Table 4 summaries the proposed policies and measures within nine sectors of four pilot low-carbon cities in municipal low carbon plans, i.e. household, transport, industry, agriculture, service, transformation, and spatial planning. Though these cities vary in leading industry, e.g., the tourist industry in Xiamen, the coal industry in Jincheng, the heavy industry in Suzhou, and the petrochemical industry in Ningbo, their low-carbon plans did not fully embody their internal differences in industrial backgrounds and energy structure, e.g., heterogeneous market-based instruments and different measures in service, transport, and industry sectors. Moreover, a relatively large variety of strategies lacked a timeline and specific targets, e.g., in sectors and enterprises, for quantifying and decomposing overall energy and GHG emissions reduction targets. This will be a challenge for achieving overall low carbon targets. 5. Discussion and suggestions 5.1. Energy-carbon nexus The comparative results from the trend line of energy usage and CO2e emissions indicate that generation of the CO2e emissions are strongly related to increasing energy consumption and GDP. These findings are also identified by other research (Lin et al., 2010; Bi et al., 2011) and make decarbonization feasible through energy consumption reduction and low-carbon economic structures. Various energy sources differ in the amount of emitted CO2. The

dependence on high-carbon energy, i.e. coal and fuel oil, results in intensive CO2e emissions, while clean fuel generate less CO2e. Hence the alternative way to mitigate GHG emissions is to optimize energy structure and enhance energy efficiency. The sectoral evaluation and projection in the proposed LEAPNingbo model show that the main contribution of energy consumption and CO2e emissions is associated with the industry and transformation sectors. This may be associated with the highcarbon fuel consumption structure in the above two sectors during rapid urbanization in China. Thus low-carbon energy resources, i.e., biomass, wind, solar, hydro, geothermal, electricity and natural gas, need to be introduced to replace high-emitting fuel oil and coal. In addition, the spread of low-carbon energy resources requires future policy priority to overcome difficulties in terms of technological barriers and economic thresholds. 5.2. Sectoral energy use-driven greenhouse gas mitigation Ningbo as a pilot low-carbon city will have to explore the carbon reduction roadmap and share successful experiences within the context of a high-carbon industrial structure and rapid urbanization. The simulation of the proposed LEAP-Ningbo model implies great potential for the reduction of energy-related CO2e emissions under the three policy scenarios (ESO, PES, INT) compared to the BAU scenario. Under the ESO, PES and INT scenarios, the emissions will be 10%, 18% and 27% lower than the BAU scenario by the end of 2050. Notably, the INT scenario performs more effectively in CO2e emissions reductions than the other three scenarios. Consequently, tailored measures in certain sectors of Ningbo can be identified and discussed for taking efficacious low-carbon actions. The household sector is little responsible for the reduction of energy use and carbon emissions due to its less than 2%

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Table 4 A comparison of sectoral policies and measures in low-carbon plans among pilot cities. Sectors

Household Transport

Industry

Agriculture

Service

Transformation

Planning

Policies and Measures

Cities

Low-carbon building; Water recycling; Renewable energy substitution Household garbage classification; Public transport supply Public transport priority; Energy-saving and environment-friendly vehicles Low-speed traffic and footpath Smart transport system; Eliminating high energy-consuming vehicles Transport infrastructure and waterway Express and logistics network Reforming high energy-consuming, high-carbon industry, eliminating outdated technologies; Recycling utilization of productive resources; Energy-saving renovation of existing buildings; Green building Recycling interregional industry system; Hardcover houses New energy resources and low-carbon industrial technologies Strategic emerging industry Strengthening leading industry Recycling urban-rural renewable resources Energy-saving management and subsidy Energy-saving lighting appliances Renewable energy in buildings Modern agricultural system with low-carbon technologies Reuse of agricultural by-products and garbage Renewable energy utilization Energy-saving agricultural machinery Increase the ratio of service industry Shipping logistics, tourism exhibition, financials, business, software and information Tourism, modern logistics, trade High-tech, business, cultural and creative, modern logistics, consumer service Increase in the ratio of clean energy; lowering coal ratio; Energy-saving and low-carbon technologies; energy efficiency Renewable energy resources; Smart power grid Thermal power; Coal-bed methane Clean coal Public green space; Forest carbon sink; Carbon emissions accounting Land consolidation; compact city Urban-rural construction plan Supervision of pilot low-carbon city Laws and regulation system of low-carbon city Low-carbon office system Carbon emissions trading system; Agricultural lands and wetlands Green city centers Marine carbon sequestration technologies Watershed ecological restoration; Afforestation projects

contribution. This proportion is just bigger than that of the agriculture sector. The household sector should popularize the efficient electrical appliances, e.g., energy-saving refrigerators, air conditioners, cooking stoves and lamps, due to fact that the electricity covers nearly 60% of the household energy mix. Notably, phasing out incandescent bulbs and replacing them with light emitting diode bulbs and compact fluorescent lamps has been enforced in many countries (Emodi et al., 2017). In addition, more clean energy, e.g., natural gas and liquefied petroleum gas, are encouraged to replace high-carbon coal. Most of the resulting GHG emissions stems from coal consumption in household life. Evidently, saving electricity in the household sector can indirectly facilitate the reduction of CO2 emissions in the transformation sector. In regard to the industry sector, optimization of the energy use structure and enhancement of energy use efficiency are feasible pathways. It is observed that the greatest potential for carbon reduction is related to the industry sector in this study. This may be related the situation of intensive industrialization and petrochemical industrial structure in current Ningbo. Reducing the share of electricity, coal and fuel oils is effective in the industrial energy saving measure and the policy-oriented energy saving scenario. Moreover, the decomposing of regional carbon reduction targets for the industry sector, e.g., local enterprises, can promote industrial energy technologies innovation and eliminate the outdated energy

Xiamen

Jincheng

Suzhou

Ningbo

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use devices. Some low-carbon and circular economy policies, e.g., demonstration projects, institutional framework, and priority areas, could be highlighted in industrial upgrading. The stimulus, e.g., the provision of financial incentives, subsidies, clean development mechanisms, and global climate funds, could help tackle short-term difficulties in industrial transformation and technology switching (Emodi et al., 2017). For the transformation sector, reducing the share of coal in the production of oil products, coal products and electricity is the priority option for energy saving and emissions mitigation. This is because coal and crude oil are main energy resources in the transformation sector. In this study, the renewable and new energy utilization measure in the energy-saving optimization scenario is most effective in emissions reduction. This implies that the increasing share of clean energy, i.e., natural gas, hydro, solar, and wind, could help reach an ideal result in carbon reduction. It is a vital carbon mitigation pathway in petrochemical cities. As to the transport sector, a more convenient and environmentally friendly transport system needs to be established due to the explosion of population and vehicles. The high-carbon gasoline and diesel fuels in the transport system will be gradually replaced by vehicle fuels such as compressed natural gas and biomass. This is also proven by the transport energy saving measure. In China, natural gas is gradually replacing piped liquefied petroleum gas.

D. Yang et al. / Journal of Cleaner Production 156 (2017) 480e490

Taxis and buses fueled by compressed natural gas are spreading quickly. Though there are still technical difficulties and economic thresholds, private cars fueled by clean fuel receive unprecedented attention. The renewable energy resources and electrified transportation are alternative options for smart transport networks (Amini et al., 2017). Among the various sources of clean energy, solar power and wind power is relatively available in Ningbo. The agriculture sector contributes little to both energy saving and carbon mitigation. The amount of energy consumption and CO2e emissions from agriculture sector are steadily increasing up to 2050 under the four scenarios, but the proportion of both them is decreasing continuously under all scenarios. Potential improvement lies in the development of biomass energy and the reduction of diesel. The service sector, similar to the agriculture and household sectors, represents a small share of total carbon emissions. The potential is associated with saving electricity in public services and replacing high-carbon fuels with clean fuels, such as solar power. Moreover, the reduction of electricity, one of the main energy resources in the service sector, means saving energy in the transformation sector. 5.3. Alternative pathways for low-carbon cities A scientific plan plays a crucial role in developing a low-carbon city by elaborating accessible targets, clear guidelines and detailed measures. Tables 1 and 4 show low carbon targets, the most popular measures, and sector-based policies for low carbon-oriented cities. Despite having prioritized low-carbon fields, the sketched plan is absent in explicit timeline-restricted targets for sectors and key enterprises. How carbon-reduction tasks can be disaggregated is still unclear. This is because of a large variety of regional differences in economic vitality, industrial structure, energy intensity and technological involvement. It shows that local governments rely more on administrative, planning and legal measures than market-oriented measures to implement the low carbon city plans (Khanna et al., 2014). Pathways for bridging the gap between lowcarbon targets and public interest receive insufficient attention. Furthermore, there is a lack of city-scale benchmarks and carbon emission inventories in most Chinese provinces and cities. This needs support from successful experiences from pilot low-carbon actions. The pilot low-carbon city program is a trial that copies the successful paradigm of China's reform and opening policy, i.e., popularizing and creation after summarizing successful pilot examples. Moreover, these policies should adhere to various city backgrounds, e.g., leading industries, development stage, local tolerance, public involvement and knowledge transfer. Especially, the economic structural effects create uncertainties in shaping a city's GHG emissions pattern. This has been discussed in references (Wang et al., 2011; Yang et al., 2012; Yang and Li, 2013; Khanna et al., 2014; Ye et al., 2017). Taking industrial structure for example, the energy city should pay more attention to low carbonoriented energy networks, while the heavy industrial city focuses more on clean energy substitution and industrial updating. For a tourist city, the low-carbon strategies, e.g., the convenient public transport, energy-saving accommodation buildings, and waste-toenergy systems, should be incorporated into city planning. They also share some promising industrial actions, such as high-tech, low-carbon, modern service and creative industries. Unlike other types of city aims initiated in China across the national scale in recent years, e.g., livable city, sponge city, resilient city, smart city and eco city (Jong et al., 2015; Zhang, 2016; Deakin and Reid, 2017), the low-carbon city addresses climate change challenges and decouples economic growth from high-carbon

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energy usage. This kind of city shares a common sustainable aim and provides livable spaces for citizens. Hence urban actions need to align different targets with common pursuits to avoid duplicating actions. In addition, the poorly understood low-carbon targets may lose the wide-ranging co-benefits of CO2 mitigation, e.g., energy security, improved productivity, increasing ecosystem services, and disease reduction (Yang et al., 2013; Khanna et al., 2014). 6. Conclusions This study proposes a LEAP-Ningbo model to identify the energy-carbon linkages and discusses the alternative sectoral policies for carbon mitigation. Four scenarios are designed to model the dynamics of energy-related CO2e emissions in six urban sectors. The integrated scenario, the energy structure optimization scenario and the policy-oriented energy saving scenario are used to check low-carbon development pathways by various measures and policies, which helps select alternative low-carbon roadmap compared to the BAU scenario. The results highlight a significant impact on energy use and GHG emissions for future low-carbon trajectories. The results show that: 1) Under the BAU scenario, the total energy demand will reach 449.72 Mtce with an annual growth rate 3.88% and result in 651.83 Mt CO2e emissions; 2) The proposed policy scenarios (i.e. ESO, PES, and INT) produce lower energy consumption (1%, 13% and 14%) and lower CO2e emissions (10%, 18%, and 27%), respectively. Not surprisingly, GHG emissions are going to reduce under policy scenarios, but with no turning point by 2050 due to insufficient policy involvement, and industrial transformation difficulties. This requires more radical policies and measures than those applied in the model if Ningbo is going to become low-carbon city in foreseeable future; 3) Since a positive nexus lies between energy consumption and resulting CO2e emissions, the innovation of energy policies, e.g., higher rate of clean energy, energy technology switching, low-carbon energy incentives, and energy appliances replacement, will play a profound role in GHG emissions reduction; 4) Overall, great potential for energy use-driven GHG reduction is associated with the industry, transformation and transport sectors in the predicted period; 5) As for energy use-related low carbon actions, energy substitution (e.g., clean, non-carbonizing, and waste-transformation energy resources) and energy efficiency improvement (e.g., fuel economy standards, carbon labels, and energy conservation standards for building) will play a dominant role in future development. The future low-carbon pathway in Ningbo city lies in clean energy substitutions and energy innovation policies. Difficulties in economic structure and energy policies, e.g., outdated industrial structure, high-carbon energy, and lagged energy appliances, are challenging the development of low carbon cites in China. Current low carbon city plans are insufficient in conveying constructive details, e.g., the timeline-restricted targets, the city-scale benchmarks, the sector-based emission inventories, and the market-oriented measures. Thus it is indispensable to develop a sound low-carbon plan that adheres to city backgrounds. Achieving low carbon targets requires joint efforts, e.g., energy substitution, industrial restructure, technology innovation, local tolerance and knowledge transfer. This study illustrates long-range GHG mitigation for low carbon cities involving suitable policies and pathways. Acknowledgement This research was supported by the National Natural Science Foundation of China (41371535, 41690142), the Natural Science Foundation of Ningbo (2013A610284), the Science and Technology Key Project of Fujian Province (2015Y0082), and the National Water

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