**3. Results**

The energy resilience of 309 Chinese cities is shown in Figure 4. The entire country was divided into four regions according to the National Bureau of Statistics of China [51], namely, the western region (107 cities), the central region (81 cities), the eastern region (87 cities), and the northeastern region (34 cities). Several cities were more resilient than the surrounding areas. There were four types for different reasons. First, provincial capital cities generally had better political resources, managemen<sup>t</sup> levels, and economic development advantages compared with their surrounding cities and thus had stronger comprehensive city strength and better performance in CI and CE. This applied to Changchun of Jilin, Harbin of Heilongjiang, Taiyuan of Shanxi, Kunming of Yunnan, and Fuzhou of Fujian. Second, Zhangjiakou of Hebei is close to the capital, Beijing, and serves as an important satellite city. It is located in the coal transport corridor, has abundant wind energy resources, has developed a number of microgrid projects, and has few energy-consuming industries, all of which made it a relatively energy-resilient city. Third, Zhuhai of Guangdong has relatively small population density, industrial density, and economic size in Guangdong province, resulting in low CV. As CE and CI were not significantly different, Zhuhai's resilience value was higher. Fourth, Shenzhen of Guangdong was more resilient within the province because of its better performance in energy diversity, microgrid projects, and development of nuclear power.

**Figure 4.** Resilience of urban energy systems for 309 Chinese cities. (Note: The gray areas were not included in the assessment because of lack of data.).

#### *3.1. Regional Level*

In general, a majority of the 309 cities, especially those in the northeastern and western regions, had relatively low energy resilience. In contrast, UESR in the eastern region was generally higher. The average resilience (R) result of the eastern region was more than twice that of the northeastern and western regions. The resilience variance (S2) of the eastern region was nearly an order of magnitude higher than that of the other three regions. The most evenly distributed cities were located in the central region. The differences in CV among the four regions were not significant in terms of average, maximum, minimum, or variance, with the eastern region only slightly higher than the other three regions. From the perspective of CE, there were no obvious distribution characteristics. The eastern region had the highest average. The central region had the lowest variance. The situations of the western and northeastern regions were similar. The highest CI average occurred in the eastern region as well. The statistics of the evaluation results are shown in Table 1. The detailed data and evaluation results can be seen in Tables S1–S4 of the Supplementary Materials.


#### *3.2. Provincial Level*

Among the evaluated 27 provinces/autonomous regions:


#### *3.3. City Level*


temples, and repositories of ancient books, pictographs, and other cultural relics. Its city competitiveness (index Fl 13-20), including the city's external connectivity, software and hardware environment, knowledge and information development level, and infrastructure construction, was in a disadvantageous position as well. These data were obtained from the Yearbook of China's Cities sponsored by the Sustainable City Committee of the China Research Society of Urban Development. According to the editor, the evaluation indices mainly reflected the competitiveness of cities in transforming from quantitative growth to qualitative sustainable development. To improve the resilience of Rikaze, this sustainable competitiveness should be comprehensively considered. Additionally, the reliability of the power supply can be improved, and the line loss rate of power enterprises can be reduced. Electricity conservation could be further advocated and executed, and new energy vehicles and enhanced transportation accessibility could be promoted. In terms of energy diversity, the use of natural gas and heat supply also lagged. However, this is related to the local climate and residents' habits and customs, which are difficult to change in the short term and require long-term adjustment and planning.

**Figure 5.** Comparison of the three cities' R/CV/CE/CI results.


#### *3.4. Regression Analysis*

Since the resilience of UESs is a critical issue in the current energy transition toward the 3060 targets, it is interesting to understand the relation among a city's energy system resilience, carbon dioxide emissions (megaton) and GDP (10<sup>10</sup> RMB).

By the weighted least squares method (weight = 1/resid2), the following binary nonlinear regression equation is obtained, and the model fits the evaluation results well.

**Figure 6.** Comparison of cities with minimum/median/maximum resilience results.

RESILIENCEi= − 0.049111 + 0.177735CO2Ei0.204 + 0.045861lnGDPi+ei

> t = (705.8698 \*\*\*) (749.1603 \*\*\*) (484.5519 \*\*\*)

#### R\_squared = 0.9999, n = 309

where \*\*\* means at 1% significant level. The empirical results showed a positive correlation between resilience and carbon dioxide emissions, suggesting that there should be a balance among loss of resilience, reduction in carbon dioxide emissions, and increase in GDP. For an example, in Yingkou, a reduction in carbon dioxide emissions of one million tons would sacrifice resilience by 0.0073 and drop the city 12 places in the ranking, and an increase in GDP of 22,949.87 million RMB would enhance resilience to maintain the original position. Therefore, in the process of achieving the 3060 targets, to ensure the safety and sustainability of a city and allow its resilience to fluctuate within reasonable limits, how to appropriately

(9) allocate the carbon dioxide emission reduction quota to each city is critical. Based on the evaluation framework of this study, the options for both reducing emissions and enhancing resilience vary from city to city. Generally, feasible alternatives include advancing the financial feasibility of the energy sector, promoting, and practicing energy conservation, and improving the managemen<sup>t</sup> of power enterprises.

## **4. Conclusions**

With the ambitious 3060 targets, China is looking forward to an unprecedented energy transition. As a core part of energy transition and sustainability, resilience must be given serious attention, especially when extreme events have occurred more frequently in recent years.

To this end, this paper implemented a nationwide comprehensive assessment of the resilience of UESs in China. The results showed that the current capabilities of Chinese UESs to handle exogenous extreme events are very uneven, and that cities in the eastern region generally have higher resilience than those in other regions. The minimum, median, and maximum UESR results corresponded to Rikaze, Yingkou, and Weifang, respectively. Regression analysis of 309 cities' resilience evaluation results showed a positive correlation among UESR, carbon dioxide emissions, and GDP. When the details of this evaluation are combined and the differences lucubrated at the urban/provincial levels, each city should develop a tailored plan to reduce carbon emissions, ensure reasonable changes in UESR, and flexibly utilize economic instruments.

The aim of this study was to establish a benchmark to understand the complicated correlations and challenges of energy transition. The findings of this study may assist municipal and provincial decision makers with unique insights for enhancing overall UESR. Moreover, continual assessments of the UESR of these cities in future years could offer policy makers much more valuable information on energy transition and urban development.

The proposed indicators mainly suit China's current reality, and different, specific indices should be adopted when the assessments are applied to cities in other countries. The results do not contain value or other judgments.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/su14042077/s1, Table S1: Resilience evaluation results of 309 Chinese cities, Table S2: CI data and results of 309 Chinese cities, Table S3: CE data and results of 309 Chinese cities, Table S4: CV data and results of 309 Chinese cities.

**Author Contributions:** Conceptualization, Z.W. and R.W.; methodology, Z.W. and Q.S.; software, Z.W.; validation, Z.W., C.M. and Q.S.; investigation, Z.W. and Z.C.; resources, Q.S.; data curation, Z.W. and Z.C.; writing—original draft preparation, Z.W.; writing—review and editing, Z.W.; supervision, R.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sources included scholarly publications, trade organization publications, research reports produced by governmental departments and educational organizations, and, when possible, direct contact with experts in related fields. In detail, the CI data sources included governmental yearbooks and bulletins at the city/provincial/country levels, the academic research results of transportation accessibility in [40], and the China Urban Construction Statistical Yearbook. The CE data sources included governmental yearbooks and bulletins at the city/provincial/country levels; the business inquiry platform www.tianyancha.com (accessed on 22 May 2021); the official website of the Ministry of Industry and Information Technology of the People's Republic of China, https://www.miit.gov.cn/ (accessed on 1 February 2022); the official website of the National Development and Reform Commission of the People's Republic of China, https://www.ndrc.gov.cn/ (accessed on 1 February 2022); and the China Urban Construction Statistical Yearbook, China Electric Power Yearbook, China Electric Power Statistical Yearbook, State Grid Yearbook, China Electric

Power Industry Annual Development Report, China Automobile Industry Yearbook, China Industrial Statistical Yearbook, Yearbook of China's Cities, and China Basic Unit Statistical Yearbook. The CV data sources included the China Urban Construction Statistical Yearbook and the China Economic and Social Big Data Research Platform, https://data.cnki.net/NewHome/index (accessed on 1 February 2022).

**Acknowledgments:** This work was supported by the Shandong University Seed Fund Program for International Research Cooperation.

**Conflicts of Interest:** The authors declare no conflict of interest.
