Peak CO₂ emission in the region dominated by coal use and heavy chemical industries: a case study of Dezhou city in China

Abstract

This paper studies the pathways of peaking CO₂ emissions of Dezhou city in China, by employing a bottom-up sector analysis model and considering future economic growth, the adjustment of the industrial structure, and the trend of energy intensity. Two scenarios (a business-as-usual (BAU) scenario and a CO₂ mitigation scenario (CMS)) are set up. The results show that in the BAU scenario, the final energy consumption will peak at 25.93 million tons of coal equivalent (Mtce) (16% growth versus 2014) in 2030. In the CMS scenario, the final energy will peak in 2020 at 23.47 Mtce (9% lower versus peak in the BAU scenario). The total primary energy consumption will increase by 12% (BAU scenario) and decrease by 3% (CMS scenario) in 2030, respectively, compared to that in 2014. In the BAU scenario, CO₂ emission will peak in 2025 at 70 million tons of carbon dioxide (MtCO₂), and subsequently decrease gradually in 2030. In the CMS scenario, the peak has occurred in 2014, and 60 MtCO₂ will be emitted in 2030. Active policies including restructuring the economy, improving energy efficiency, capping coal consumption, and using more low-carbon /carbon free fuel are recommended in Dezhou city peaked CO₂ emission as early as possible.

Keywords

carbon dioxide emission / energy consumption / peak CO₂ emission / low-carbon transition / Dezhou city / China

Introduction

China has become one of the largest energy consumer and greenhouse gas (GHG) emitter in the world. As a result, it now faces a huge international pressure to respond to climate change by reducing GHG emissions. At the same time, China’s domestic environmental concerns, such as acid rain and haze resulting from coal consumption also pose major challenges. In 2014, the total energy consumption and carbon dioxide (CO₂) emission in China amounted to 4.26 gigatons of coal equivalent (Gtce) and 9.76 gigatons of CO₂ (GtCO₂), respectively. These accounted for 23.0% and 27.5% of the global energy consumption and CO₂ emission, respectively, with China ranking first globally for both. Additionally, in the same year, the per capita energy use in China reached 3 tons of coal equivalent (tce) and the per capita CO₂ emission exceeded 7 tons of CO₂ (tCO₂), close to levels for the European Union and Japan [¹–³]. The Paris Agreement adopted in December 2015 requires that the global average temperature rise be controlled at below 2°C above pre-industrial levels and efforts be made to limit the rise to 1.5°C. In June 2015, China submitted Enhanced Actions on Climate Change: China’s Intended Nationally Determined Contributions (INDC) to the Secretariat of the United Nations Framework Convention on Climate Change. The document officially promises that the country will reach a peak CO₂ emission level around 2030 while making efforts to peak early, increase the share of non-fossil fuels in primary energy consumption to 20%, and reduce the CO₂ emission per unit of gross domestic product (GDP) by 60% – 65% from the 2005 level [⁴]. In addition, nationwide, the Chinese economy is now facing excess production capacities, and multiple policies have been issued to fuel healthy economic growth; e.g., supply-side reform, de-capacity of energy-intensive sectors, and the upgrading of high-value-added sectors. These goals pose a huge challenge to Chinese economic development but also create unique opportunities for the country to undergo a low-carbon industry transformation in the future.

China has substantial inter-regional differences in energy consumption and CO₂ emission, with remarkably higher per capita levels in the eastern coastal region than in the inland provinces of central and western regions [¹]. Therefore, to reach the national goals of CO₂ emission control, the emission peak must be accomplished first in the eastern coastal region. Shandong is a large industrial province in East China and a major energy user and source of CO₂ emission [⁵]. Dezhou is a prefecture-level city of Shandong province with an economy currently ranking below the average among cities of the province. The per-capita GDP of Dezhou city has considerably lagged behind that of leading peers, such as the provincial capital Jinan [⁶]. In 2014, the total primary energy consumption in Dezhou city was 25.78 million tons of coal equivalent (Mtce) and 86% from coal consumption, with a 51% increase from the 2005 level. The energy consumption per capita and CO₂ emission per capita in Dezhou city are 4.59 tce 11.81 tCO₂ respectively, both of which are higher than the national average levels [²]. Locally, Dezhou city has issued a series of polices to promote its economic and social development, new energy and renewable energy development, low-carbon development during 2010–2020 [⁷–⁹]. Dezhou city has been a national leader in solar energy products manufacturer. In September 2005, the city was awarded the Solar City of China by three national associations because of its famous solar energy products (solar heater and PV panels). In June 2010, it became a pilot city of the Swiss-Chinese Low-carbon City Demonstration Program. With these honors, the city has aimed to explore an innovative green pathway for industrial development, with low energy consumption, low carbon emission, high energy efficiency, and high environmental friendliness, thus contributing to the overall fulfillment of the national promise on the GHG emission target made to the world. In its low-carbon transition, however, the city faces one major challenge. Because it is reliant on heavy chemical industries, and its economy ranks middle nationwide and below average within the province [⁵,⁶], its rapid economic growth inevitably remains a crucial task and accomplishing a low-carbon transition (particularly for its industries) while maintaining rapid economic growth poses a challenge to the local city government [⁷–⁹]. In this economic context, how to maintain its industrial competitiveness while simultaneously fulfilling its environmental responsibilities (i.e., peak energy use and CO₂ emission, time to peaking, and the trend after peaking) will be regarded as a demonstration to other cities in China [¹⁰,¹¹].

A number of researchers have developed future scenarios of China’s national energy consumption and CO₂ emission using integrated assessment models. For example, Chen et al. [¹²,¹³] explored China’s Greenhouse gas (GHG) emissions roadmap in 2050 using MARKAL/TIMES. Jiang et al. [¹⁴] analyzed low carbon development pathways in China by employing IPAC-AIM. Zhou et al. [¹⁵] examined energy use and CO₂ emissions of China’s industrial sector from a global perspective by using integrated-assessment model (the global change assessment model (GCAM)). Energy efficiency has become a significant issue and several studies have been conducted focusing on the role of energy efficiency in realizing the CO₂ emissions reductions under the more aggressive pathway using the LEAP model [¹⁶,¹⁷]. Besides, the world energy model (WEM) was established to explore energy outlook in China [¹⁸]. Pan et al. [¹⁹] also used GCAM on China’s energy system transformation under global 2°C goal. Yuan used the KAYA method for China’s 2020 emission target on national level [²⁰,²¹]. Zhang et al. [²²] and Li et al. [²³] focused on China’s historical carbon flow or emission trading on national level. On province level, Xiao [²⁴] and Ren et al. [²⁵,²⁶] used the carbon-emissions-coefficient method to calculate carbon emissions in Shandong province in historical emission or urbanization impact. Zhou [²⁷], Oberheitmann [²⁸], Zhou and Li [²⁹], and Jiang et al. [³⁰] used the KAYA method, the complete decomposition model, the log mean divisia index (LMDI) method, based on several high aggregated factors, such as population, GDP, energy intensity per unit of GDP, and CO₂ emissions of per unit of energy, respectively. On cities level, limited research are available with only one research on solar water heaters in the context of Dezhou city [³¹]. It can be observed that the studies summarized here are integrated at national or regional levels, thus providing no detailed sectorial information. Consequently, most studies do not reflect rapid developments in subsequent years that have impact on China’s current macroeconomic, industrial de-capacity, energy efficiency, and policy requirements. Besides, there is also little available information on city-levels and their contributions to China’s announced peak emissions target (e.g., by year and emission amount), while the energy consumption and CO₂ emission of such areas are huge and important to China’s announcement on peak emission target.

This paper aims at investigating the future energy consumption and CO₂ emission in Dezhou city by employing scenario analysis. Specifically, it addresses three questions. First, when will CO₂ emissions of Dezhou city peak? Second, at what level will CO₂ emissions peak and what is the role of industrial emissions in the long-term? Finally, how can a peak emission target be met in Dezhou city?

This paper explores the future peak CO₂ emission in Dezhou city using a regional bottom-up calculation model based on the energy intensity, energy structure, and sector output of Dezhou city, which affect the future emission pathway for the city. The novelty of this paper include detailed analysis on city-level CO₂ emission controlling and consideration of current changing of national and local economy development. The bottom-up energy demand method modeling, the intensive on-site field data investigation, and the applicable promotion policy discussion can safeguard the novelty of the research method, the reliability, and the advantage of the scenario analysis of Dezhou city, as stated in the next sections.

Methodology and major parameters

Many approaches have been developed to study energy consumption and CO₂ emission [¹²–³²], typically including top-down model (i.e., computable general equilibrium) and bottom-up model (i.e., MARKAL/TIMES, LEAP). The study mentioned in this paper aims to identify the important influencing factors by analyzing historical and current data, and simulate the future development by appropriate extrapolation of relevant parameters. Given that the data availability, especially the cost and other economic parameters associated with various energy technologies, are not available for provincial/sub-provincial study in China, a LEAP-type approach is suitable in this study [⁸].

Model principle

The present study analyzed the energy consumption and demand by the sectors (i.e., industrial, building, transportation, and power) with an econometric model employed whose framework is shown in Fig. 1.

In the LEAP-type approach, sector intensity analysis can describe the relationships between energy use and carbon emission, economic output, energy service, and population for investigation of their future development and general interrelations.

For a given sector, the output related with GDP or population will be the key driver factor for end-use sectors (major product in the industrial sector, floor space in the building sector, and turnover or number of private vehicles in the transportation sector). Each sector will be divided into several sub-sectors based on the sector characteristics. For example the industrial sector includes steel, cement, chemical, petroleum, paper, and other subsectors (including mining and agriculture); the building sector includes the public and residential subsectors; the transportation sector includes the passenger, the freight, and the private vehicle subsectors. For each subsector, its yield of a major product is first analyzed and then, when the future output or major product of that sector and energy intensity per unit of product are determined, the total energy use by the sector can be readily calculated. Besides the final energy demand sectors, the power sector is analyzed based the balance of demand and supply. In the end, the primary energy consumption and total CO₂ emission are calculated.

Calculation equations

Final energy demand

The final energy demand sector is divided into the following sector and subsectors. The three end-use sectors refer to the industrial (including manufacturing, mining, and agriculture in China energy statistical yearbook), transportation, and building (including residential and services). The details of each subsector are listed in Table 1.

Based on the characteristics of different sectors, for the year t, the total final energy demand is the sum of three end-use sectors_i as expressed in Eq. (1),

(1){\mathrm{FE}̲\mathrm{Total}}^t={\displaystyle \sum _{i=\mathrm{1}}^{\mathrm{3}}{\mathrm{FE}̲\mathrm{Sector}}_i^t},

where {\mathrm{FE}̲\mathrm{Total}}^trepresents the total final energy demand in year t and {\mathrm{FE}̲\mathrm{Sector}}_i^t is the final energy demand of end-use sectors i in year t. For each sector, {\mathrm{FE}̲\mathrm{Sector}}_i^t is the sum of Sub-sector_i,j as shown in Eq. (2). Hereby, the subsectors of industry, building, and transportation are listed in Table 1.

(2){\mathrm{FE}̲\mathrm{Sector}}_i^t={\displaystyle \sum _j{\mathrm{FE}̲\mathrm{Subsector}}_{i,j}^t}.

For each sub-sector, {\mathrm{FE}̲\mathrm{Subsector}}_{i,j}^tis calculated by multiplying the output (economic value/goods) by energy intensity (EI) as

(3){\mathrm{FE}̲\mathrm{Subsector}}_{i,j}^t={\mathrm{Output}̲\mathrm{Subsector}}_{i,j}^t\cdot {\mathrm{EI}̲\mathrm{Subsector}}_{i,j}^t,

where for the year t, {\mathrm{FE}̲\mathrm{Subsector}}_{i,j}^t is the total final energy demand of sub-sector_i,j, {\mathrm{Output}̲\mathrm{Subsector}}_{i,j}^tis the output of sub-sector_i,j, and {\mathrm{EI}̲\mathrm{Subsector}}_{i,j}^tis the energy consumption per unit of output of sub-sector.

For the subsectors of the industrial sector (excluding the power sector), the energy consumption analyses can be performed by taking two approaches. The first approach obtains the energy use by multiplying the output of the major product and the energy use per unit of product. The second approach obtains the energy use by multiplying the industrial value added and the energy use per unit of GDP. The output of sub-sector is defined as the number of product units (million t) or value added (CNY billion, at a constant price for 2014). The unit-product energy intensity for sub-sector_i,j, is defined as the amount of energy used (tce/t product) per unit of product in that sub-sector (i.e., steel, cement, chemical, petroleum, or paper) or other sectors as energy used per unit of value added (Mtce/billion CNY).

For the subsectors of the building sector, the energy intensity is expressed as the energy use per unit of floor space (kilograms of coal equivalent per m²; kgce/m²). The output is expressed as the floor space (m²).

For the transportation sector, the energy intensity is expressed as the energy use per unit of transportation traffic turnover or per private vehicle (toe/10000 person∙km, toe/10000 tons∙km, or toe/private vehicle). The output is expressed as the transportation turnover or number of private vehicles.

For each sector, the energy intensity for unit product production has been calculated from relevant official statistical yearbooks, which are detailed in Section 3. The data describing the future development of a sector have been obtained by combining the information on macroeconomic trends, governmental planning, and historical development and experiences in China.

The final energy demand of each sub-sector can be divided by the final energy fuel type (k) as shown in Eq. (4), and the total final energy demand of three sectors by the final energy fuel type (k) can be summed from each sub-sector as shown in Eq. (5).

(4){\mathrm{FE}̲\mathrm{Fuel}}_{i,j,k}^t={\mathrm{FE}̲\mathrm{Subsector}}_{i,j}^t\cdot {\mathrm{Share}̲\mathrm{Subsector}}_{i,j,k}^t,

(5){\mathrm{FE}̲\mathrm{Total}}_k^t={\displaystyle \sum _{i=\mathrm{1}}^{\mathrm{3}}{\displaystyle \sum _j{\mathrm{FE}̲\mathrm{Fuel}}_{i,j,k}^t,}}

where for the year t, {\mathrm{FE}̲\mathrm{Fuel}}_{i,j,k}^tis the final energy type k consumed by each sub-sector, {\mathrm{FE}̲\mathrm{Total}}_k^tis the total final energy type k consumed by the sub-sectors, {\mathrm{Share}̲\mathrm{Fuel}}_{i,j,k}^tis the proportion of the final energy type k in the total final energy consumed by sub-sectors. Fuel type (k) refers to coal, oil, gas, and electricity.

Power sector

For the power sector, the total electricity demand is the sum of the electricity demand of sub-sector_i,j as shown in Eq. (5). And the electricity supply should be balanced with the electricity demand, considering the lost factor of electricity transmission. The electricity supply generally includes three parts, onsite fossil generation (coal, oil, and gas), onsite renewable electricity, and electricity imported from provincial grid, as shown in Eq. (6).

(6)$$\begin{gathered} {\mathrm{Ele}̲\mathrm{Supply}}^t={\displaystyle \frac{{\mathrm{FE}̲\mathrm{Total}}_{\mathrm{electricity}}^t}{\mathrm{1}-{\mathrm{Loss}}_{\mathrm{transmisson}}}} \\ ={\displaystyle \sum _k{\mathrm{Ele}̲\mathrm{Onsite}}_k^t}+{\mathrm{Ele}̲\mathrm{Import}}^t \\ +{\mathrm{Ele}̲\mathrm{Renewable}}^t, \end{gathered}$$

where for the year t, {\mathrm{Ele}̲\mathrm{Supply}}^t is the total electricity supply, {\mathrm{Loss}}_{\mathrm{transmisson}} is the lost factor of electricity transmission, {\mathrm{Ele}̲\mathrm{Onsite}}_k^t is the onsite fossil generation (coal, oil, and gas), {\mathrm{Ele}̲\mathrm{Import}}^t is the electricity imported from provincial grid, and {\mathrm{Ele}̲\mathrm{Renewable}}^t is the onsite renewable electricity.

In Dezhou city, the difference between the primary energy consumption and the final energy demand is the energy conversion loss, which mainly cones from the power sector. Therefore, considering the electricity imported and renewable electricity, the electricity of final energy demand from coal, oil, and gas onsite are converted into primary energy consumption as

(7){\mathrm{PE}̲\mathrm{Ele}̲\mathrm{Onsite}}_k^t={\displaystyle \frac{{\mathrm{Ele}̲\mathrm{Onsite}}_k^t}{{\mathrm{efficiency}}_{\mathrm{electricity},k}^t}},

where for the year t, {\mathrm{PE}̲\mathrm{Ele}̲\mathrm{Onsite}}_k^t is the fossil consumption (coal, oil, and gas) of the power sector onsite, and {\mathrm{efficiency}}_{\mathrm{electrcity},k}^t is the efficiency of fossil generation k (coal, oil, and gas).

Primary energy consumption

Based on the calculation results of the final energy demand and power sector consumption, the total primary energy consumption can be summed from the fossil energy demand of each final sector and the power sector, plus the electricity imported and renewable electricity onsite, divided by primary energy type k as

(8){\mathrm{PE}̲\mathrm{Total}}^t={\displaystyle \sum _{k=\mathrm{coal,oil,gas}}({\mathrm{FE}̲\mathrm{Total}}_k^t+{\mathrm{PE}̲\mathrm{Ele}̲\mathrm{Onsite}}_k^t})+{\mathrm{Ele}̲\mathrm{Import}}^t+{\mathrm{Ele}̲\mathrm{Renewable}}^t,

where {\mathrm{PE}̲\mathrm{Total}}^t is the total primary energy consumption. For other terms in Eq. (8), please refer to Eqs. (1) to (7).

CO₂ emission

The CO₂ emission is obtained by multiplying the energy consumption and the emission factor per unit of energy. CO₂ emission can be calculated based on the total primary energy consumption of fossil energy (coal, oil, and gas), multiplied by different CO₂ emission factors with different energy types, plus the indirect CO₂ emission from the electricity imported, respectively, as

(9){\mathrm{CE}̲\mathrm{Total}}^t={\displaystyle \sum _{k=\mathrm{coal,oil,gas}}{\mathrm{CEF}}_k\cdot ({\mathrm{FE}̲\mathrm{Total}}_k^t+{\mathrm{PE}̲\mathrm{Ele}̲\mathrm{Onsite}}_k^t)}+{\mathrm{CEF}}_{\mathrm{import}}\cdot {\mathrm{Ele}̲\mathrm{Import}}^t,

where {\mathrm{CE}̲\mathrm{Total}}^t is total CO₂ emission, {\mathrm{CEF}}_k is the CO₂ emission factor of energy type k (coal,oil and gas), and {\mathrm{CEF}}_{\mathrm{import}} is the CO₂ emission factor of the electricity imported from provincial grid. The emission from renewable electricity is zero CO₂ emission.

Scenario designing of future development

Considering uncertainties in the future economy and the requirement of low-carbon development, two scenarios, a business-as-usual (BAU) scenario and a CO₂ mitigation scenario (CMS) scenario, for Dezhou city were studied. In the two scenarios, the future GDP of the city and its growth rate are generally the same, featuring a gradually decreasing energy use per unit of GDP with time. In the CMS scenario, the growth of secondary sectors decelerates gradually but more rapidly than that in the BAU scenario. At the same time, the growth in service sectors in the CMS scenario increases more rapidly than that in the BAU scenario. To reach the national goal of CO₂ emission target of reaching a peak CO₂ emission level around 2030, the emission peak must be accomplished first in the eastern coastal region. Dezhou is a prefecture-level city of Shandong province in the eastern coastal region with a per capita CO₂ emission higher than the national average level, the peak emission year must come earlier than 2030. Therefore, the time span in this paper is from 2014 to 2030 for the peak energy and CO₂ emission in Dezhou city.

The BAU scenario assumes that the city follows its past mode of economic growth but with a modest structural adjustment and upgrading. Industrial sectors, especially the energy-intensive sectors, are assumed to follow the current trends in the future. In this scenario, the GDP growth rate for Dezhou city will be slightly higher than that for the nation and Shandong province soon. With the deceleration in industrial growth, the GDP growth rate for the city will then be similar to that for Shandong province. Compared with the case for 2014, the output of energy-intensive sectors till 2030 would remain unchanged. Moreover, the scenario assumes that the industrial structural adjustment and upgrading will be promoted, the outdated production facilities will be closed, and the equipment manufacturing sector of high-value-added products will be fostered as a new source of economic growth. In this scenario, the demand for electricity grows continuously, and the new demand will be met by external purchasing (importing) combined with the development of low-carbon electricity generation.

The CMS scenario assumes that the strategy of low-carbon development is rigorously followed, the environmental and resource control is strictly implemented, and the growth of energy-intensive sectors is strictly restricted or the output is reduced. In this scenario, although the future GDP of the city and its growth rate are generally the same with BAU in total, the GDP growth of secondary sectors for the city will be moderately slower than that in the BAU scenario in the near future; subsequently, with the completion of economic adjustment and upgrading and the booming of tertiary sectors, the tertiary sectors would grow more quickly than the secondary sectors and become a major contributor to the GDP as Table 2 demonstrates. The outputs of energy-intensive sectors in 2030 will decrease by 10% – 20%, compared with the 2014 level. Furthermore, local coal generation will be reduced by 10% (2030 versus 2014), and the electricity gap will be met by importing power from the provincial grid and the generation of renewable electricity.

Key data, parameters, and assumptions

The major parameters and data used include the population and urbanization rate, GDP and its growth, and the status and future trends of sectors (industrial, building, transportation, and power) as presented in Tables 2 – 4. The data not readily available are also estimated as described below.

Population and urbanization rate

Dezhou city covers an area of 10356 km², and the local population growth has been slow, with the population increasing from 5.40 million in 2001 to 5.83 million in 2014 (annual growth of 6.0‰). In 2014, the permanent urban population was 2.83 million (urbanization rate of 48.4%), with an urbanization rate similar to that at the national level [⁶].

Demographic studies show that the population of Dezhou city will grow slowly and the urbanization rate further increase. The population will peak at 6.23 million in 2025 and then remain stable till 2030. According to Urban System Planning of Shandong Province (2011–2030) [³³], by 2030, the urbanization rate of the city will reach 75%, with 4.7 million permanent urban residents.

GDP and growth

GDP and economic status

The economy of Dezhou city ranks lower middle in the province, and is clearly dominated by heavy chemical industries. In 2014, Dezhou city recorded a GDP of 260 billion CNY and GDP per capita of 45500 CNY, and was ranked 14th among 18 prefecture-level cities in Shandong province. The primary, secondary, and tertiary industries of the city contributed 27.1 (10.4%), 131 (50.3%), and 108 (39.3%) billion CNY, respectively. In comparison, nationwide in 2014, the value added by secondary and tertiary industries accounted for 42.7% and 48.1% of the national GDP, respectively. The dominance of secondary industry (7.6% higher than the national average) and lower contribution from tertiary industry (8.8% lower) indicate that the economic structure of the city was dominated by heavy chemical industries [⁶]. Historically, the contribution of primary industry and secondary industry decreases gradually, whereas the contribution of tertiary industry is increasing from 2005 to 2014 as shown in Table 2 [⁶].

These data indicate that Dezhou city has followed a path of extensive economic growth via the expansion of heavy industries. As a result, its economy is strongly characte-rized by high investment, high GDP output, low value added, high energy intensity, and severe pollution. These characteristics peaked around 2007. Subsequently, with adjustment and upgrading of the economic structure, the contribution of the tertiary industry increases gradually. Currently, five energy-intensive subsectors (chemical, steel, cement, petroleum, and paper) are the leading economic sectors of the city, accounting for approximately one-third of the industrial GDP and 80% of the industrial energy consumption.

Rate of economic growth

The rate of local economic growth has been relatively high. From 2001 to 2014, the annual growth rate consistently exceeded 10% and was higher than the average rate for the province and the country (for constant prices with a base year of 2014). During this period, the growth rate for Shandong province consistently exceeded the national level (by 2.3%), and the growth for Dezhou city consistently exceeded the provincial level (by 1.6%) [⁶].

Economic development

Studies on the Chinese economy predicted GDP growth is approximately 6.5% during 2015 – 2020, 5.5% during 2020 – 2025, and 4.5% during 2025 – 2030, with a decrease of 1% every 5 years [¹⁰,³⁴,³⁵]. The growth rate for Shandong province has been estimated to be 0.5% – 1.0% higher than the national average, and the growth for Dezhou city 0.5% – 1.0% higher than that for the province.

From 2015 to 2030, because the growth of secondary industry is expected to decelerate, the growth of the total GDP of the city will approach provincial and national averages. More specifically, the GDP growth will be approximately 9.0% during 2015 – 2020, 7.0% during 2020 – 2025, and 5.5% during 2025 – 2030. In 2030, the local total GDP will reach 788 billion CNY (130000 CNY per capita), tripling that in 2014. In 2030, the economic structure of the city is expected to have a GDP ratio of primary, secondary, and tertiary industries of 6:52:42 in the BAU scenario, compared with 6:42:52 in the CMS scenario [¹⁰].

Industrial sector

Current energy consumption by industrial sector

The industrial sector is the largest end-user of energy in Dezhou city. The energy consumption and emission of this sector will thus largely determine the future energy consumption and CO₂ emission of the city. In 2014, the industrial sector (excluding the power generation subsector) consumed 15.16 Mtce, accounting for 67% of the total final energy consumption in the city. Furthermore, five energy-intensive subsectors (chemical, steel, cement, petroleum, and paper) consumed 12.30 Mtce, accounting for 80% of the total energy consumption by the industrial sector [⁶].

Energy intensity by sector

To simplify the analyses, the major products generated by the industrial sector (steel, cement, chemical, petroleum, and paper) have been counted as crude steel, cement, ammonia, oil, processed crude oil, and paperboards, respectively. Consequently, energy intensities have been expressed as the amount of energy required to produce one unit of the products. Because of the availability of information, statistical data of 2005 – 2014 have been used in this study. The data reveal that the energy intensities for these products decreased by 10% – 30% during 2005 – 2014 [⁶,¹⁰]. Such trends clearly cannot continue limitlessly and should instead gradually decelerate tending to lower limits. It is thus assumed that the energy intensities will further decrease by 5% – 20% from 2014 to 2030.

The energy intensities for agriculture and “other” industrial subsectors have been determined from the energy consumption per unit of value added. Statistical data reveals that the energy intensities decreased by 50% during 2005 – 2014. It is, therefore, assumed that they will further decrease by approximately 50% from 2014 to 2030 [⁶,⁸,¹⁰]. The detailed information are given in Table 3.

Energy structure

Figure 2 shows the energy structure of the industrial sector of Dezhou city in 2014. Generally, coal was the dominant energy in these subsectors, but the contributions of various energies (i.e., coal, oil, gas, and electricity) vary substantially. The steel, cement, chemical, and paper subsectors use predominantly coal (above 80%) followed by electricity [⁶]. The chemical plants in Dezhou city predominantly use coal because their primary operation of ammonia synthesis uses coal as raw material. In comparison, the petroleum plants use crude oil as feed stock. Despite these differences, generally, energy-intensive subsectors (except petroleum) predominantly used coal with limited consumption of other energies, whereas the “other” industrial subsectors primarily use coal and electricity. For simplicity, the energy structure (fuel share) of each subsector has been assumed generally unchanged from 2015 to 2030, although the energy efficiency will improve gradually as demonstrated in Table 3. Because China is currently facing excess capacity on energy intensive sectors at a national level, which implies that it is less likely to build new energy intensive factories, i.e., build new steel facility using electricity as fuel instead of coal or coke in steel sector in the years to come. In addition, in petroleum subsector and chemical subsector, coal or crude oil are used both as energy fuel and feedstock and as raw material (non-energy use). Because, no detailed information for the energy use and non-energy use are available in Dezhou city, approximate estimations are adopted in this paper. For the petroleum subsector, the national level of about 90% of oil used for feedstock has been used in this paper [²], which means that about 90% of oil consumption is excluded for feedstock, and the rest 10% of oil consumption are included in the final energy consumption and CO₂ emission. For the chemical subsector, although there are complex chemical products in general, most of the energy fuel and feedstock consumption (more than 70%) are from ammonia production used coal as raw material in Dezhou city. Because feedstock of coal in ammonia production will be consumed and emitted CO₂ finally in process-related, no distinction is made between fuel and feedstock with all emissions accounting for in the chemical subsector [³⁶]. With no detailed information available in Dezhou city, all the coal consumption (energy fuel or feedstock) has been taken as the final energy consumption and CO₂ emission in the chemical subsector in this paper.

Outputs of energy-intensive products

The Chinese economy currently faces excess capacity. Research shows that, on national level, the outputs of energy intensive industrial products are generally reaching the peak in 2014, while the floor space in the building sector and the service in the transportation sector will continue to increase gradually [¹⁰,³⁴,³⁵]. Because Dezhou city lies in the east of China with a middle nationwide economy, the BAU scenario assumes that the outputs of the steel, cement, chemical, paper, and petroleum subsectors will remain unchanged during 2014 – 2030 in this study. In the CMS scenario, the output of each of these subsectors is assumed to be 20% less in 2030 than in 2014, and the outputs in intermediate years are determined by linear interpolation [⁶,⁷,¹⁰]. The detailed information is tabulated in Table 4.

Building sector

The building sector includes residential and public buildings. The energy consumption by the building sector has been estimated by multiplying the building floor space and energy intensity. The data of the area of residential buildings have been obtained from official statistics. The information related to the area of public buildings and energy intensities of both residential and public buildings are unavailable, and has thus been estimated as follows.

The area of buildings has been calculated by multiplying the building area per capita and the population size of the city. More specifically, the area of residential buildings per capita for Dezhou city was 36 m² in 2014 and is estimated to reach 40 m² in 2020 and to remain at this level thereafter. The area of public buildings per capita was 3.3 m² in 2014 and is estimated to reach 4.1 m² in 2030 similar to the provincial average [⁶,¹⁰].

The energy intensities have been taken from public studies [³⁷–³⁹]. For residential buildings, the energy intensity for heating (coal), cooking (coal plus limited natural gas), and electrical appliances (electricity) were 14.33, 2.37, and 1.46 kgce/m² in 2014, respectively. For public buildings, the annual energy intensity for heating (coal) and electric equipment were estimated to be 16.61 and 8.45 kgce/m² in 2014, respectively, and the annual energy intensity for cooking were little and ignored. The energy intensity for heating is expected to change minimally with time; the energy intensities for other uses are assumed to double from 2014 to 2030, as a result of the constant demand for an improved quality of life [⁶,¹⁰].

Transportation sector

The energy consumption by the transportation sector included the energy used for passenger, freight, and private vehicles. The former two have been calculated by multiplying the respective energy intensity with the passenger and freight traffic turnover, and the last one has been estimated according to the energy intensity and number of private vehicles. The number of private vehicles and traffic turnovers for passenger and freight have been obtained from a statistical department. The energy intensities are unavailable and have been estimated using national average values (i.e., fuel use per unit turnover, and annual fuel use per private vehicle) [¹,²,⁴⁰].

Road transportation is the major type of transportation in Dezhou city, and the fleet of registered vehicles will inevitably grow rapidly. Because the official statistical criteria for traffic turnover were changed after 2008, the data from 2009 onward have been used in this paper. From 2009 to 2014, the passenger turnover increased from 5.4 to 7.2 billion (person·km), with an average annual growth of 6.0%. Meanwhile, the freight turnover increased from 28.8 to 40.8 billion (tons·km), with a moderately greater average annual growth (7.3%) compared with passenger traffic [⁶]. From 2014 to 2030, the passenger and freight traffic turnover is estimated to further increase by approximately 90% [⁶,¹⁰].

There were 340000 private vehicles in Dezhou city in 2009 and 580000 in 2014, with an average annual growth of 12%. This growth is greater than the increases in passenger and freight transportation. The average vehicle ownership was moderate in 2014 (100 vehicles per 1000 residents) and is estimated to increase to 240 vehicles per 1000 people in 2030 (total fleet of 1.42 million vehicles), approaching the ownership of Beijing in China in 2014 [¹]. In the simulation, the annual fuel consumption of private vehicles has been assumed to be 0.96 t gasoline per vehicle (i.e., the national average level in 2014) and decrease by 20% in 2030. Similar assumptions of energy intensity have been made for passenger and freight transportation [⁶,¹⁰].

Power sector

The energy consumption by the power sector has been analyzed as follows. From 2005 to 2014, the local annual power generation increased from 17.0 to 17.4 TWh [⁶], indicating a stable power supply and production. Moreover, coal generation contributed 97% of the local power generation whereas the contribution from non-fossil fuels (less than 3% of the total electricity) was obviously below the national average (more than 20% of the total electricity) owing to the scarcity of hydropower resources. The renewable electricity was 0.86 TWh in 2014, which will increase to 4 – 5 times in 2030 [⁷]. In fact, in 2014, the total local demand for electricity was 20.7 TWh, leaving a 3.3 TWh gap (i.e., 16% of total demand) that was met by importing power from the provincial grid. Because the city lacks local coal sources and China is currently faceing excess capacity on power on national level, it is, therefore, assumed that the local fossil power generation would remain stable until 2030. The future growth in demand for electricity from end-use sectors (industry, building, and transportation) could be imported from the provincial power grid or obtained by local renewable electricity generation.

Results and analyses

Final energy demand

Tables A1 and A2 in Appendixes indicate the final energy demand by sector and fuel type in the BAU scenario and CMS scenario.

Final energy demand by sector

With the completion of the urbanization of Dezhou city, the growth in final energy demand will decelerate after 2020, as depicted in Fig. 3. In the BAU scenario, the final energy demand will increase gradually to 25.93 Mtce in 2030, featuring a 50% increase from 2010 and a 16% increase from 2014. In the CMS scenario, the final energy demand will peak at 23.47 Mtce around 2020, and decrease 5% to 22.35 Mtce in 2030. The peak energy demand will occur 10 years earlier and be 9% lower in absolute amount in the CMS scenario than in the BAU scenario. In both scenarios, the final energy demand of the building and transportation sectors will increase continuously. For the building sector, the final energy demand will increase 27% – 33% from 2014 to 2030, and its share in the total final energy demand will increase 4% – 7%. Meanwhile, the final energy demand of the transportation sector will increase 65% – 74%, enlarging its share by 4% – 5%. For the industrial sector, the total final energy demand will remain stable in the BAU scenario and decrease in the CMS scenario from 2014 to 2030 (more details are given in Sub-section 3.3). In 2014, the industrial sector accounted for 68% of the total final energy demand; in 2030, the share will decrease to 60% in the BAU scenario, compared with 56% in the CMS scenario.

Final energy demand by fuel type

Figure 4 indicates that the total final energy demand by fuel type would comprise predominantly of coal, but the share of low-carbon energies would increase gradually. In 2014, coal alone accounted for 75% of the total final energy demand, and non-coal energies contributed 25% (oil: 10%, gas: 3%, electricity: 12%). After 2014, the output of non-energy-intensive sectors would increase, thus enlarging the shares of gas and electricity in the energy structure substantially. In the BAU scenario, in 2030, coal, oil, gas, and electricity will account for 66%, 12%, 5%, and 17% of the total final energy demand, respectively. Thus, compared with 2014, the share of coal would decrease by 9% while the shares of oil, gas, and electricity increase by 2%, 2%, and 5%, respectively. The CMS scenario is found to have similar changes, suggesting that the final energy demand evolves spontaneously toward a lower-carbon, greener, and more diversified structure even in the absence of climate policy intervention or GHG emission restrictions. In both scenarios, in absolute amount, the electricity and gas in 2030 will increase by 50% – 80%, while coal in 2030 keeps the 2014 level in the BAU scenario and decreases by 13% in the CMS scenario, which implies that, in the CMS scenario, coal are reduced and controlled more strictly, while electricity and gas are encouraged in the future, which is consistent with the latest governmental policy of Beijing-Tianjin-Hebei area air pollution control, because Dezhou city belongs to one of the “2+26” cities under this regulation [⁴¹].

Energy demand by industrial sector

Toward the completion of industrialization around 2020, the growth of the energy demand of the industrial sector would decelerate (Fig. 5). The industrial sector consumed 11.2 Mtce of energy in 2010 and 15.2 Mtce in 2014. Subsequently, the growth would decrease. In the BAU scenario, the energy demand of the industrial sector is expected to peak at 16.9 Mtce in 2025 and remain nearly constant until 2030. In the CMS scenario, the energy demand of the industrial sector would decrease gradually, reaching 12.6 Mtce in 2030, which is 20% lower than that in the BAU scenario. With continuous improvement in technology, energy efficiency, and the economic structure, the energy intensity per unit of GDP will also decrease gradually. The share of the energy demand of energy-intensive subsectors among the total industrial energy demand has already peaked in 2014 at 81%, and will gradually decrease to 73% in 2030. In the CMS scenario, the share of the energy demand of energy-intensive subsectors will behave similarly, suggesting that the less-energy-intensive and high-value-added subsectors will gradually but spontaneously gain a greater share. By this process, the local economy will have an improved structure featuring lower-energy uses and more value added.

Energy demand of the building sector

Figure 6 shows that the energy demand of the building sector will increase continuously. The building sector consumed 4.92 Mtce of energy in 2010 and 5.60 Mtce in 2014. In 2030, the sector will consume 7.48 Mtce in the BAU scenario, compared with 7.10 Mtce in the CMS scenario. The two scenarios have similar energy structures, implying that the building sector will evolve spontaneously toward a lower-carbon and cleaner mode of energy demand.

Energy demand of the transportation sector

The energy demand of the transportation sector (Fig. 7) will increase continuously in the future. The transportation sector consumed 1.2 Mtce in 2010 and 1.60 Mtce in 2014. In 2030, the energy demand will reach 2.79 Mtce in the BAU scenario and 2.65 Mtce in the CMS scenario. In 2014, the energy use of the sector comprised of 88% of oil, 8% of gas, and 4% of electricity. In 2030, the two scenarios will have similar energy structures for this sector.

In 2014, private vehicles consumed 0.82 Mtce of fuel. In the BAU scenario, the fleet will consume 1.61 Mtce in 2030. Additionally, the passenger and freight transportation consumed 0.78 Mtce in 2014, and this value will increase by 52% to 1.18 Mtce in 2030. Therefore, an increase in the energy demand of transportation is expected to be attributed to private vehicles. A similar trend is predicted for the CMS scenario.

Electricity demand and supply in the power sector

The local electricity demand in 2014 (Fig. 8) was 20.7 TWh, of which 16% (3.3 TWh) was imported from the provincial power grid. From the demand side, the industrial and building sectors accounted for 74% and 24% of the total demand respectively, whereas transportation and agriculture contributed negligibly. Given the current status of the excess production capacity of the fossil power subsector, further growth in local fossil electricity generation is deemed highly unlikely in the near future. Local fossil electricity generation is, therefore, assumed to remain stable until 2030. Although the electricity demand of the industrial sector will decrease a little (versus 2014), the electricity demand of the building sector will increase remarkably. As a result, the total electricity demand will increase, and the share of imported electricity will also increase to meet the local power demand. In 2030, the total local electricity demand will be 36.3 TWh in the BAU scenario including 43% supplied by the electricity imported, compared with the 31.4 TWh demand including 36% supplied by the electricity imported in the CMS scenario. In either scenario, the percentage of the electricity demand that is supplied by the electricity imported will be substantially higher than the level of 16% in 2014. For the renewable electricity, it was 0.86 TWh in 2014, which will increase 4–5 times in 2030. The electricity imported in 2005 is negative in Fig. 8, indicating that electricity was exported to the provincial power grid in that year.

Total primary energy consumption

In 2014, Dezhou city consumed a total of 25.78 Mtce of energy (Fig. 9). (Electricity from coal generation has already been converted and included in the coal equivalent of primary energy; hereby, electricity refers to the total electricity imported (positive) or exported (negative). There is little local renewable electricity. More specifically, coal, oil, gas, renewable electricity, and the electricity imported accounted for 85.6%, 9%, 3%, 0.4%, and 2% of the total local energy consumption, respectively. In the BAU scenario, the total local energy consumption will increase slowly, reaching 28.79 Mtce in 2030 (i.e., approximately 11% growth versus 2014), of which, coal, oil, gas, renewable electricity and import electricity will contribute 22.03, 3.11, 1.32, 0.42 (34.4 TWh) and 1.90 Mtce (15.49 TWh), respectively. Compared with the quantities in 2014, the consumption of coal will decrease by 1% in 2030, whereas the consumptions of oil, gas, and the electricity imported will increase by 40%, 74%, 300% and 250%, respectively, which means that even in the BAU, all the increase of energy are from oil, gas, and imported electricity. In the CMS scenario scenario, by comparison, the coal consumption in 2030 will decrease by 15%, while the oil, gas, and electricity increase dramatically. And the total local energy consumption will peak at 26.55 Mtce (3% increase versus 2014) in 2020 and gradually decrease subsequently. In 2030, the total local energy consumption will be 24.92 Mtce, featuring an approximately 6% decrease from the peak level.

CO₂ emission and its peak in Dezhou city

Corresponding to the total energy consumption, the total CO₂ emission in Dezhou city (Fig. 10) was 66 MtCO₂ in 2014. From an emission-source point of view, in 2014, coal (including electricity generation and heating) accounted for 91% of the total CO₂ emission, and was followed by oil (7%). In contrast, the use of natural gas and the electricity imported contributed minimally to the local CO₂ emission (where the electricity imported results in indirect emission, and the emission factor is the provincial average CO₂ emission level). This implies that the local CO₂ emission and energy structure are in a high-carbon mode currently.

From the point of view of future emission, simulations show that the CO₂ emission will increase slowly in the BAU scenario, peak in 2025 (total emission: 70 MtCO₂; a 5% of increase versus that in 2014), and remain stable until 2030. In the CMS scenario, the emission peaked in 2014, and it will decrease gradually to approximately 60 MtCO₂ in 2030, there being a 14% reduction compared with the BAU scenario.

Therefore, compared with the BAU scenario, the peak emission in the CMS scenario will occur approximately 10 years earlier and be 4 MtCO₂ lower. Moreover, the emission gap between the two scenarios will increase after peaking, resulting in an approximately 10 MtCO₂ lower emission in the CMS scenario than in the BAU in 2030.

Compared with the primary energy consumption stated in Sub-section 4.3, the CO₂ emission will reach the peak about 5 years earlier than the peak year of the primary energy consumption in both scenarios, correspondingly.

Discussion

CO₂ emission per capita and per unit of GDP

Simulations demonstrated that the CO₂ emission per capita (Fig. 11) peaked in 2014 (at 11.8 tCO₂), and would decrease subsequently to 8.7 – 10.0 tCO₂ in 2030, which was moderately higher than the national average level.

The emission intensity per unit of GDP will decrease continually. Compared with the intensity in 2005, it was reduced by 36% in 2010 and 50% in 2014. It the BAU scenario, it will further reduce by 68% in 2020 and 82% in 2030, compared with the 69% and 85% reductions in the CMS scenario. In both scenarios, the decrease in emission intensity per unit of GDP will be faster than the overall target set for whole China. There are two reasons for the faster emission reduction in Dezhou city. The first reason is that the emission intensity reduction in 2014 has already reached the national emission reduction target (the CO₂ emission intensity in 2020 will be reduced by 40% – 45% from the 2005 level), which is at least 6 years earlier than the national emission target year. The second reason is that the output of the energy intensive sector has already peaked in 2014, which remains unchanged in the BAU scenario or decreases by 10% – 20% in 2030, comparing with the 2014 level, with the energy efficiency improvement listed in Table 3, and the CO₂ emission will peak at least 10 years earlier than the national target year of 2030.

CO₂ reduction contribution

It should be noted that a clean energy structure can help reduce the CO₂ emission resulted from energy consumption, especially when renewable electricity is promoted. The CO₂ emission in the CMS scenario will keep decreasing, since renewable electricity generated will have zero CO₂ emission.

Compared with the BAU scenario, the CO₂ emission in the CMS scenario in 2030 will be reduced by 9.48 MtCO₂, which is 14% lower than that of the BAU scenario. As shown in Fig. 12, the contribution to CO₂ emission reduction from coal, oil, gas, and the electricity imported are 88%, 5%, 2%, and 4%. This implies that most of the CO₂ emission reduction will come from the reduction in coal consumption.

Concluding remarks

Conclusions

In a word, the total energy consumption in Dezhou city will peak during 2020 – 2030 at 26 – 28 Mtce, which is a 3% – 11% increase from the 2014 level (25.78 Mtce). The CO₂ emission will peak during 2014 – 2025 at 66 – 70 Mt CO₂. Compared with the national target of peak CO₂ emission level around 2030, the peak year of CO₂ emission in Dezhou city will come 5 – 16 years earlier.

The total energy consumption in the BAU scenario will increase continually to 2030 at 28 Mtce, which is an 11% increase from that of 2014. Comparatively, in the CMS scenario, it will peak in 2020 at 26 Mtce, which is 8% lower than the peak level in the BAU scenario; subsequently, it will decrease and reach 25 Mtce in 2030.

The total CO₂ emission in the BAU scenario will peak in 2025 at 70 MtCO₂, which is a 5% increase from the 2014 level (66 MtCO₂). In the CMS scenario, it has peaked in 2014 and will decrease gradually to reach 60 tCO₂ in 2030, which is a 14% reduction relative to the level predicted in the BAU scenario for 2014. In 2030, the total emission would be 10 MtCO₂ lower in the CMS scenario than in the BAU scenario.

In this paper, the energy intensity (energy efficiency) and fuel structure are estimated from the national level because local information are not available. For the chemical sector, becasue coal consumption used as energy fuel and feedstock is regarded as energy consumption, although no distinction is made between fuel and feedstock with CO₂ emissions, it does overestimation coal consumption used as energy use to some extent. Investigations of local detailed information on the technical level are highly desirable in order to elucidate the result of this issue.

In both scenarios, the peak year of emission are 2025 in the BAU scenario or 2014 in the CMS scenario, which are generally earlier than the target year of 2030 of the China INDC announcement under Paris Agreement climate change mitigation, which has an important implication for China that the contribution of the local city to the national emission target is feasible and acceptable. To guarantee the realization of the NDCs targets from the point of view of the local city, policy recommendations are proposed as follows.

Policy recommendations

In the past ten years, the growth of primary energy consumption and CO₂ emission has slowed down because of the local energy efficiency improvement effort and industrial adjustment measures. But with the living standard improving in the near term, the energy consumption from the building sector and transportation sector will still increase inevitably. Based on the results obtained in this paper, the following recommendations are proposed to reach peak energy consumption and peak CO₂ emission as early as possible.

To establish a local cap for total energy consumption and CO₂ emission

The government should actively promote low-carbon production, domestic living, and socioeconomic development while ensuring no (or minimal) negative effects on economic growth and household living level. By supporting this low-carbon transition, the government should be able to cap the total energy and coal consumptions under 26 Mtce and 21 Mtce, respectively, and peak consumption should be reached around 2020. Accordingly, the total CO₂ emission should be capped under 66 MtCO₂.

To increase the shares of natural gas, renewable energies, and the electricity imported

The local production of primary fossil energies should be maintained at the current level (7.5 Mtce, 30% of the total energy consumption), and the demand-supply gap can be closed by developing renewable energies and by importing natural gas and electricity. These practices should help increase the share of non-fossil energies in the total primary energy consumption to 8% and the share of natural gas to above 5%, and control the share of coal to below 75%.

To foster the transition to low-carbon industrial sectors and limit energy-intensive sectors

Policies should be developed to adjust the local economy pattern and structure of industrial subsectors to foster a transition to less-energy-intensive and higher-value-added production. By eliminating outdated production capacities and restricting the expansion of energy-intensive sectors, the local industrial subsectors will be driven toward the production of less energy-intensive and better-value-added products. The energy share of energy-intensive sectors should reduce from 81% in 2014 to 73% in 2030.

To reduce the carbon intensity of energy structure and improve energy efficiency

Policies should should be made to direct the city toward a low-carbon, clean, and efficient consumption of energy. Specifically, the industrial use of lower-carbon energies (e.g., natural gas and electricity) should be increased. Energy conservation should be promoted as a top priority. Electricity demand should be met primarily by developing local renewable energies (e.g., wind, solar, biomass, and geothermal electricity) and importing power from the provincial grid.

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