EXECUTIVE SUMMARY

In 2020, Chinese President Xi Jinping announced, at the 75th United Nations General Assembly, that China will aim to peak carbon dioxide (CO2) emissions before 2030 and achieve carbon neutrality by 2060. At the national level, China has – over the past decade – made remarkable progress in renewable energy development, in particular in solar photovoltaics (PVs) and wind power. By the end of 2020, renewable electricity accounted for around 30% of total electricity output and 42% of national installed power generation capacity. In spite of this, fossil fuels remain dominant in Chinese energy use, including in the power sector and in end-use sectors such as transportation, buildings and industry. Cities, including their suburban areas, where the majority of these sectors’ activities take place, will therefore have an important role to play in achieving the goals announced by President Xi Jinping.

INTRODUCTION

With the near-universal adoption in 2015 of the Paris Agreement – an international treaty on climate change – reaching net-zero emission of anthropogenic carbon dioxide (CO2) around 2050 has become the key driver for the global energy transition (IPCC, 2018; UNFCCC, n.d.). Cities will play a critical role in reducing emissions as they are responsible for around threequarters of global energy use and CO2 emissions (Edenhofer et al., 2014). Cities would, in return, be rewarded with new opportunities for industrial and business development, as well as job creation, generated by implementing innovative solutions and redesigning urban infrastructure to unlock potential for carbon emissions reduction (IRENA, 2020a).

China’s new carbon targets are guiding cities’ energy development
Five years after joining the Paris Agreement, Chinese President Xi Jinping announced, at the 75th United Nations General Assembly, that China will aim to achieve carbon neutrality1 by 2060 and peak (energy-related) CO2 emissions before 2030. These are known as the “dual carbon goals” or “30-60 goals” in China. They send a strong signal of China’s commitment to accelerating efforts to address climate issues under the global framework set out by the Paris Agreement.

Therefore, it would be easier for the northern and western cities with abundant renewables to decarbonise their energy supply and provide renewable electricity to the other regions, if the transmission capacity permits. Zhangjiakou city is one such example (see Box 1). For many central and eastern Chinese cities (except for some coastal cities with decent offshore wind energy potential), the local potential for renewable energy resources is modest; furthermore, the available land for the deployment of utility-scale renewable energy systems is limited. Nevertheless, thanks to their geographic proximity to the load centres and continued decline in cost, there are still some cities where local renewable-based distributed energy systems present compelling options.

Wuzhong District: A pilot on the race to net-zero
Wuzhong, located in the city of Suzhou (illustrated in Figure 1), is a rapidly developing district with a population of approximately 1.1 million (accounting for 10% of Suzhou’s total population). The district has an area of 2 231 square kilometres (km2), of which the land area is 745 km2 and the rest is Lake Tai. Eighty-seven per cent of the total area is classified as an ecological conservation area, including
61% of the land area. For conservation areas, stricter environmental assessment procedures and
environmental regulations apply for any type of development.

The district is divided into four zones: the Lake Tai touristic zone, the economic and technology development zone, the agricultural zone, and the high-tech industrial zone. The industrial and service sectors are the key pillars of Wuzhong’s economic development, driving gross domestic product (GDP) growth over the past years at a rate higher than the national average. Due partly to high service sector shares in its economic structure compared with other districts in Suzhou, Wuzhong’s energy intensity per unit of GDP is about 10% lower than the city average. Nevertheless, it has two energy-intensive industries: chemical fibre production and ferrous metal processing. Their shares in the total energy consumption of Wuzhong are rather modest, at 10% and 12%, respectively, because of their small scales in capacity. By contrast, the electronic equipment manufacturing industry and the textile industry, which are low in energy intensity, each account for 25% of the district’s total energy use, as a result of their relatively larger production scales (Wuzhong DRC, 2020).

Objectives of this report
The overall objective of this report is to provide a framework for discussion that focuses on strategies, technologies and measures to decarbonise the energy system at the district level. This framework can help urban energy planning decision makers at the district level to engage in balanced discussions about increasing sustainable energy development towards the carbon neutrality target, preferably reaching the objective of becoming a net-zero carbon emissions district. The recommendations offered in this report are suggestive rather than prescriptive, as the situation in China is rather dynamic and energy transition technologies are rapidly evolving globally. It is important to remain flexible when developing a longer-term strategy.

METHODOLOGY

This study takes a two-step approach to address the challenges that Wuzhong District faces during its transition towards a net-zero carbon future. The first step is to explore what options Wuzhong would have if the district used conventional emission abatement technologies to reduce its carbon emissions, considering Wuzhong’s limited potential for local renewable energy resources and its currently dominant fossil fuel consumption. A long-term optimisation energy system model was built to construct multiple planning scenarios. The scenario results show how the emissions from the current generation fleet can be reduced as much as possible through conventional approaches such as efficiency improvements, fuel switching, and modest increases in the use of local renewable energy resources. This will help local decision makers understand the limitations of the business-as-usual approach and why transformative measures might be needed. The objective function (total cost and/or carbon) is minimised subject to constraints, which can be specified to include bounds on technology capacities, operation and capacity factors, as well as resource potential. Balanced constraints ensure that energy demands are satisfied by installed technology capacities in each time period and time slice.

Structural scope
The structural scope of the built model is summarised in Table 1.

Technology scope
The scope of energy conversion technologies under consideration is described in Table 2 by output or functional category. These technologies include the existing and future investment technology options in the model. Detailed descriptions of these technology categories and their advantages, disadvantages and key parameters are well documented by the International Energy Agency’s ETSAP programme (IEA-ETSAP, 2014).

Data constraints, estimation and assumptions
As with many countries, the availability and accessibility of energy data in China can be a greater challenge at the district level than at the national or provincial levels. This is in part due to the lack of sound energy statistics systems for urban energy system planning, which is – albeit rising worldwide – still relatively novel for local authorities, particularly in the Chinese energy governance regime. The challenges are often attributed to the need for higher data granularity within cities for energy analysis and planning. Such highly granular data are hard to acquire for various reasons (e.g. privacy issues). In addition, when the shares of variable renewable energy sources increase in the energy mix, the need for more detailed data on both the supply and demand side becomes greater.

To fill gaps in data collection for this study, proxy data, historical trends and other publicly available and published data were adapted to fit Wuzhong’s profile. These data have been calibrated with
supplied reference data. Assumptions were also made on the basis of the expert knowledge and insights obtained for this study, particularly for projecting future demands. The methods, assumptions and resulting data inputs applied in the model are detailed in the following sections.

Electricity demand for industrial sector
The hourly industrial load profile was created from hourly data for the industrial sector of a district whose industrial structure is similar to that of Wuzhong.2 The data indicate a baseload consumption pattern. We adopted this hourly baseload profile as a reasonable approximation for our model. Figure 3 indicates a baseload consumption pattern, providing a reasonable approximation for the model.

Electricity demand for service and residential sectors
The total electricity load profile for the service and residential sectors is derived from the total electricity load profile in 2017 minus the hourly industry electricity load, followed by further disaggregation based on the residential and service load profiles. The resulting profiles are adjusted to match available annual total electricity consumption data. Figure 4 represents the hourly electricity demand profile for the service sector; Figure 5 shows the profile for the residential sector.

Process heating demand for industrial sector
The industrial process heat load profiles were generated by applying the ratios of process heating load among the different seasons to the seasonal shares of the industry electricity load shown in Figure 3. The hourly load curve (Figure 6) represents a typical day of operation for a regional heating system in industry (Li et al., 2018). This is calibrated with the total process heating demand.

Efficiency improvements are also considered for electrical appliances based on projected improvements until 2050 in European studies (Kirchner et al., 2012). Improvements in lighting, refrigeration, washer-dryers, TVs and other appliances are applied to Chinese electricity usage statistics to determine net efficiency improvements over time (Guo, Khanna and Zheng, 2016). The resulting efficiency improvement rates relative to 2020 over time are illustrated in Figure 25 for residential, service and industrial sectors for average and high efficiency cases. Service and industrial sector improvements appear relatively limited because only lighting efficiency gains are considered in these sectors (due to limited data availability for other end-use converters).

Thermal and gas networks
The district heating network in Wuzhong primarily serves industry and is, therefore, considered for the industrial sector only. Heat imports from the surrounding areas are considered only in the base year. Data on maximum annual natural gas imports based on existing and future infrastructure were not available. Therefore, the model imposes an upper limit of 10 terawatt hours (or 36 petajoules) of gas imports in the base year; this limit scales with increasing total energy demand until 2050.

MODELLING SCENARIOS AND RESULTS

Baseline case
The BC represents a scenario without an emission or renewable energy development target. Therefore, the optimisation in the BC uses only one modelling objective: minimising the total system cost to meet the given energy demand. It includes all the generation technology options in the technology scope when performing the optimisation, except for CCS. The key current decision on fuel switching, which essentially aims to phase out coal power in the district, has also been factored in. On the energy efficiency side, the BC scenario adopts average improvement measures, which are assumed to meet the two-star green building standard for new residential buildings and the three-star standard for commercial and service buildings. In the transport sector, given the overall rapid pace of vehicle electrification in China, this scenario assumes 20% and 50% of the transportation fleet in Wuzhong District is electrified by 2035 and 2050, respectively.

Figure 27 illustrates the modelling results for the BC scenario. As shown, electricity from the regional and national grids will be expected to increase by about 50% over the projected period, providing the most cost-effective electricity to meet growing electricity demand in Wuzhong. For the first half of the studied period, electricity from the local gas-fired power plants is expected to scale up to meet the demand growth, except for the initial transition phase from coal to gas in power generation in 2021-2022. This also fits the overall national strategy to use natural gas to replace coal as a transitional step towards renewables. For the second half of the projected period (i.e. 2036-2050), renewable electricity will be expected to take off and replace gas due to its strong cost competitiveness. Therefore, the combined share of gas-fired electricity and renewable electricity over the next three decades would make up the remaining quarter of the supply for Wuzhong in the BC.

In this scenario, the energy performance requirements for residential and commercial buildings are implemented at the minimum required rate in the model. Yet we would see a rise in the use of heat pumps in residential and commercial or service buildings. Heat pumps will take off, regardless of emission constraints, thanks to the economic benefits they can bring through improved efficiency of energy use and the growing demand to regulate room temperature for comfort under future climate change conditions, where extreme weather patterns would occur more frequently and more severely than before. For Wuzhong, both individual units and the centralised application of heat pumps would be adopted subject to the demand density in a given area within Wuzhong.

Carbon policy
The carbon policy (CP) scenario describes a case in which the Wuzhong energy system is driven by an objective to reduce carbon emissions by 15% by 2040 compared with 2020 levels – a target recommended in carbon peak guidelines for Shanghai and Suzhou by the Chinese National Center for Climate Change Strategy and International Cooperation (Cao et al., 2019). This scenario is not intended to achieve net-zero emission by 2050 but rather to present a portfolio of energy technologies for achieving gradual emission reductions under a suggested CP while keeping cost minimisation as an optimisation objective. This scenario includes improved energy efficiency measures for end-use appliances and threestar building energy performance standards for new residential and service or commercial buildings. It also includes a high transportation electrification rate of 40% by 2035 and 70%
by 2050.

As with the BC scenario, coal-fired power generation has no role to play in this scenario after its phase out in the early 2020s. In terms of generation from solar PV by 2050, the difference between the BC and CP scenarios is rather small, given the limited total resource potential available in Wuzhong, as indicated in Figure 28. The important advancement in the CP scenario is that the solar PV generation will scale up significantly earlier than in the BC scenario and will reach its full potential in about 2040. Only mainstream technologies such as rooftop PV are modelled, which suggests potential room for growth of solar PV by applying advanced and innovative technologies such as building-integrated PV (BIPV) on building envelopes and other city-integrated solar solutions.

In the CP scenario, imported electricity from the grid will account for a significant share of the electricity mix in the second half of the projected period in view of the overall decarbonisation of the grid electricity mix in China. In the medium term, however, gas-fired power plants through CCP and CHP will have an important role to play in achieving the CP objective, under the assumption that the carbon intensity of grid electricity4 will not dramatically decline until 2035, after which low-carbon energy generation is planned to significantly accelerate towards achieving net-zero goals by 2060.

Source:IRENA