2.1. Policy background

The current Chinese administrative framework primarily comprises three tiers of governance, namely provincial-level, prefectural-level and county-level administrative regions. While the overall hierarchy has remained stable, the government has frequently adjusted urban administrative boundaries to promote urbanization. These adjustments mainly take two forms: county-to-city upgrading (che xian she shi) and city-county consolidation (che xian she qu). County-to-city upgrading refers to elevating a county to city status, whereas city-county consolidation involves converting counties or county-level cities into municipal districts under a prefecture-level city, thereby expanding the city’s jurisdiction over land and population. Before 1997, county-to-city upgrading dominated administrative reforms; however, stricter approval standards substantially reduced its use thereafter. Since then, city-county consolidation has become the primary instrument for government-led urbanization, with two major waves occurring during 2000–2003 and 2013–2016. Figure A1 (online appendix) illustrates the evolution of merged counties in China from 1999 to 2021.

2.2. Literature review

We will present a comprehensive review of three distinct strands of literature that are pertinent to this paper. Firstly, we will review research on the policy effect of city-county consolidations, which is important for understanding a potential connection between consolidations and energy efficiency. Secondly, we will introduce the literature on factors that influence energy efficiency. Lastly, we will conduct an examination of existing research that explores the impacts of urbanization on energy utilization, which is vital to understanding how urbanization affects energy usage patterns.

Existing studies generally regard city-county consolidation as an important governance tool that achieves economies of scale and enhances administrative efficiency through institutional integration. Early research focused primarily on fiscal performance and public service provision (Reingewertz, Reference Reingewertz2012), finding that administrative consolidation typically reduces managerial redundancies and optimizes resource allocation, thereby improving public service efficiency and lowering administrative costs (Allers and Geertsema, Reference Allers and Geertsema2016). Specifically, consolidation can enhance the scope and quality of government services by centralizing administrative functions, unifying resource allocation and streamlining decision-making processes, while simultaneously reducing duplicated infrastructure and administrative expenditures. Research also indicates that the effects of consolidation vary with context: under mandatory policy settings, administrative integration is more likely to yield significant efficiency gains, whereas in voluntary mergers, the effects are often limited due to insufficient incentives or variations in local autonomy (Blesse and Baskaran, Reference Blesse and Baskaran2016).

As research has progressed, scholars have increasingly examined the broader impacts of city-county consolidation on economic development, population mobility, urbanization, and environmental and energy outcomes. Existing studies indicate that administrative integration can enhance factor allocation and facilitate the flow of capital and labour (Hall et al., Reference Hall, Matti and Zhou2019), thereby boosting local economic dynamism and urban development, though these effects exhibit substantial temporal and spatial variation (Lu et al., Reference Lu, Chen and Zhang2017). In China, the city-county consolidation policy is considered an important instrument for promoting county-level urbanization and regional integration. Empirical evidence shows that, following implementation, consolidated counties experience accelerated economic growth and urban expansion in the short term, while attracting population inflows; however, the long-term effects may diminish, and there is a potential risk of exacerbating regional disparities (Liu et al., Reference Liu, Zeng and Zhou2019). At the same time, some studies have begun to explore the environmental and energy consequences of city-county consolidation, finding that administrative boundary adjustments can not only improve urban air quality (Jiang et al., Reference Jiang, Chen, Yu, Zhong, Dai and Shen2022) but also reduce energy intensity (Li et al., Reference Li, Yan, Zhang, Sun and Zhang2021b). Collectively, these studies highlight the multidimensional effects of city-county consolidation. However, they mainly examine macroeconomic, spatial and urban-level outcomes, leaving county-level energy efficiency and its underlying mechanisms insufficiently explored.

A comprehensive examination of related research suggests that the determinants impacting energy efficiency can be categorized into two major factors, namely factors driven by market forces and related to policy. Research on market factors has focused mainly on technological progress (Trianni et al., Reference Trianni, Cagno and Worrell2013; Wang et al., Reference Wang, Wang, Li and Feng2019); the extent of trade liberalization (Montalbano and Nenci, Reference Montalbano and Nenci2019); urbanization level (Li et al., Reference Li, Fang and He2018; Wang et al., Reference Wang, Wang, Li and Feng2019); industrial structure (Xiong et al., Reference Xiong, Ma and Ji2019); economic development level (Filippini and Hunt, Reference Filippini and Hunt2015); energy price (Herrerias et al., Reference Herrerias, Cuadros and Orts2013); and energy utilization pattern (Yu et al., Reference Yu, Zhou and Yang2019; Zhao et al., Reference Zhao, Guo and Zhao2019). For instance, Liu et al. (Reference Liu, Zhang, Zhang and Wang2020) comprehensively analysed many factors that influence energy efficiency in Chinese provinces, providing a detailed examination of these determinants. As noted by Du et al. (Reference Du, Matisoff, Wang and Liu2016), a feasible strategy for improving energy efficiency involves increasing investments in research and development. Meanwhile, policy measures such as environmental regulation, local governance innovation and regional development strategies have been shown to effectively improve energy efficiency by guiding factor allocation and incentivizing green investment (Han et al., Reference Han, Han, Shi, Su, Lv and Lei2018b; Wu et al., Reference Wu, Hao and Ren2020; Li et al., Reference Li, Han, Wang and Sun2022).

Urbanization represents an important macro-level context for changes in energy consumption and efficiency. Most existing studies suggest that the urbanization process is typically associated with rising energy consumption, particularly during the middle stage of industrialization (Sadorsky, Reference Sadorsky2013; Bilgili et al., Reference Bilgili, Koçak, Bulut and Kuloğlu2017). Industrial agglomeration and lifestyle changes resulting from urban expansion may increase energy demand and reduce energy efficiency (Wang and Yang, Reference Wang and Yang2019). However, some research indicates that once urbanization reaches a stage of high-quality development, technological progress and infrastructure improvements can enhance energy utilization efficiency (Li et al., 2021). In the context of China, urbanization remains at an energy-intensive stage, and its net effect on energy efficiency is still debated. Recently, a few studies have approached the issue from an efficiency perspective, examining the impact of urbanization on total factor energy efficiency. They suggest that rapid urbanization may, in the short term, inhibit improvements in energy efficiency through factor misallocation and spatial congestion effects (Li et al., Reference Li, Fang and He2018; Lv et al., Reference Lv, Chen and Cheng2020).

Based on the above literature, two insights emerge. Firstly, research on city-county consolidation has gradually expanded from fiscal and economic performance to environmental and sustainable development outcomes, yet studies examining energy efficiency in the consolidated regions remain limited. Secondly, while the energy efficiency literature has revealed the roles of market and policy factors, few studies have incorporated administrative institutional changes into the analytical framework. In other words, existing research has not systematically examined how administrative boundary adjustments affect regional energy efficiency through multiple economic channels. This paper aims to enrich the literature on the economic consequences of administrative boundary adjustments and deepen the understanding of the intrinsic links between regional institutional reforms and green development.

4.1. Construction of the staggered difference-in-difference model

City-county consolidation reform is primarily driven by prefecture-level states in China. In the context of municipalities that govern counties, county-level governments have little influence on the decision-making of such reforms. Therefore, our study explores the causal effect of urbanization on energy efficiency regarding city-county consolidation from 2000 to 2017 as an exogenous shock for merged counties based on the staggered difference-in-difference (DID).

The counties that executed a city-county consolidation throughout the research phase are deemed our treatment group, whereas the counties that did not carry out any consolidation are treated as the counterfactual group. By conducting a pre- and post-policy comparison of differences between our experimental and control groups, we evaluate the energy efficiency implications of city-county consolidation. Referring to Li et al. (Reference Li, Lu and Wang2016) as well as Ma and Mao (Reference Ma and Mao2018), we build the benchmark model as follows:

(1)\begin{equation}le{e_{i,t}} = {\beta _0} + {\beta _1}pos{t_{i,t}} + {\beta _2}{X_{i,t}} + {\sigma _i} + yea{r_t} + {c_i} \times t + {\varepsilon _{i,t}}.\end{equation}

In Formula (1), $i$ represents the county or municipal district, $t{\text{ }}$denotes the year, $le{e_{i,t}}$ represents the county’s energy efficiency and $pos{t_{i,t}}$ indicates whether the county is the object of the city-county consolidation. We set $pos{t_{i,t}}{\text{ }}$to 0 before and 1 after the merger. $yea{r_t}$ indicates the fixed effect of the year, ${\sigma _i}$ indicates the fixed effect of districts and counties, ${X_{i,t}}$ represents the control variables and ${\varepsilon _{i,t}}$ is a random disturbance term. To avoid omitting factors related to cities that change with time, the fixed effect of city*year is controlled. To mitigate the impact of heteroscedasticity as well as other confounding factors on our statistical analysis, a logarithmic transformation is employed on all continuous variables.

4.2. Variable selection

4.2.3. Control variables

Economic growth is frequently characterized by a substantial escalation in the use of energy; however, it also serves a crucial role in the enhancement of energy efficiency. The manner, as well as the extent to which residents consume energy resources, might be influenced by a multitude of factors, like population density as well as income levels. All these factors may affect energy efficiency arising from a consolidation. Moreover, it is worth noting that the fiscal revenues and expenditures of consolidated counties can exert an influence on energy efficiency through the implementation of their economic policies. In summary, we refer to Liao et al. (Reference Liao, Fan and Wei2007) and Sener and Karakas (Reference Sener and Karakas2019) and select the control variables listed in Table 1.

Table 1.

Descriptive statistics

4.3. Data sources

Most data involved in empirical research (GDP, added value of the three types of industries, population, administrative area, resident savings deposit balance and so forth) are collected from the China County-level Statistical Yearbook, China Regional Statistical Yearbook and relevant prefecture-level statistical yearbooks. We collect detailed information about the policy of city-county consolidation based on the website of the Ministry of Civil Affairs of the People’s Republic of China.Footnote ¹ Since this website announced the approval year of the reform, there has been a delay in the actual construction and operation of the districts approved by the State Council (Lu et al., Reference Lu, Chen and Zhang2017). This article uses a corresponding lag period of policy approval as the implementation year of the policy.

5.1. Statistical description

All data underwent the following processing steps after its collection. Firstly, we deleted related samples of four municipalities (Chongqing, Tianjin, Beijing and Shanghai). Secondly, by comparing different statistical yearbooks, the apparent outliers in some statistical yearbooks were corrected. After processing, the panel data of 1630 counties (districts) from 2000 to 2017 were obtained for 18 consecutive years. Among them, 77 counties had undergone city-county consolidation, accounting for approximately 4.72% of the aggregated quantity of samples.Footnote ² Table 1 presents the descriptive statistics of the main variables.

5.2. Parallel trend test

To obtain valid estimates from a staggered DID framework, treated and control counties must follow parallel trends in energy efficiency prior to consolidation. We examine this assumption using an event-study specification following Li et al. (Reference Li, Lu and Wang2016) and Beck et al. (Reference Beck, Levine and Levkov2010). Figure 1 reports the event-study estimates for 10 periods before and after consolidation. The pre-treatment coefficients are statistically insignificant and display no systematic pattern, indicating that energy efficiency trends are parallel across early-treated, later-treated and never-treated counties. In contrast, post-treatment coefficients become significantly negative, suggesting that city-county consolidation reduces energy efficiency.

Figure 1.

Time trend of city-county consolidation policy.

Recent studies caution that conventional pre-trend tests have limited power and may provide misleading inference (Roth et al., Reference Roth, Sant'Anna, Bilinski and Poe2023). We therefore complement the event study with the sensitivity analysis proposed by Rambachan and Roth (Reference Rambachan and Roth2023), which evaluates the robustness of treatment effects to potential violations of parallel trends. Figure A3 (online appendix) presents the sensitivity results for the implementation year under both relative deviation and smoothing constraints. The estimated negative effect remains statistically significant even when allowing for moderate pre-treatment trend deviations, confirming that our findings are robust to departures from the parallel trends assumption.

5.3. Benchmark regression

Table 2 illustrates the outcomes of our benchmark regression, which serves as a crucial aspect of our research investigation. Column (1) displays the outcome without any control variables incorporated. The result suggests a significant decline in energy efficiency within the consolidated counties following the city-county consolidation. The significance of the ${\text{ }}post$ coefficient persists at the 1% level in column (2), where the city*time fixed effect is included as the control variable. Column (3) presents our outcomes of adding county-level control variables. The $post$ variable’s coefficient exhibits both a similar level of significance and sign as that of the first column. This shows that the merger policy has indeed reduced the consolidated counties’ energy efficiency. This finding contrasts with some existing studies. Previous literature has primarily focused on the positive effects of administrative boundary adjustments on economic growth, investment efficiency and resource allocation (Allers and Geertsema, Reference Allers and Geertsema2016; Liu et al., Reference Liu, Zeng and Zhou2019; Han and Wu, Reference Han and Wu2024). In contrast, our empirical results show that city-county consolidation may lead to a short-term decline in energy efficiency. This discrepancy may reflect higher management costs during the initial phase of the consolidation and local governments’ short-term preference for economic expansion during resource reallocation. Empirically, our study confirms that city-county consolidation can exert structurally negative effects on energy performance in the short run, offering a novel perspective on the multidimensional economic consequences of such policies.

Table 2.

Benchmark regression results

Notes: The values enclosed in parentheses indicate county-level clustered standard errors.

5.4. Robustness tests

5.4.1. Instrumental variables regression

Potential issues such as bidirectional causality, omitted variables and measurement error may bias the baseline regression estimates. A common approach to address these concerns is to employ an instrumental variable (IV) and perform a two-stage least squares estimation. We construct the IV for city-county consolidation by interacting each county’s distance to the prefecture-level city centre with a dummy indicating whether adjacent counties have undergone city-county consolidation. The first-stage results confirm strong instrument relevance, and the second-stage estimates indicate that the consolidation effect remains significantly negative. Weak instrument and under-identification tests further support the validity of the IV strategy (see Table 3).

Table 3.

Instrumental variable estimation results

Notes: The values enclosed in parentheses indicate county-level clustered standard errors. Values in square brackets are p-values, and values in curly brackets correspond to the 10% critical value for weak identification tests.

5.5. Heterogeneity analysis

5.6. Mechanism tests

Turning to mechanisms, our paper considers the three main channels: energy consumption, industrial transfer and economic agglomeration. We use the county’s per capita energy consumption, a county-level aggregate of the added value produced by the tertiary as well as secondary sectors, as well as the labour force density per unit of land area in a county, to measure these three mechanisms.Footnote ³ Here, we collect data on the three indicators and conduct regression analyses on city-county consolidation, respectively.

5.6.1. Energy consumption

The consolidation accelerated the urbanization of the merged counties. Urbanization requires large quantities of energy-intensive products. In addition, the prefecture-level municipal governments built more infrastructure to strengthen the connection between the urban areas and the merged counties. Meanwhile, residents’ income increased and their consumption changed to include more energy-consuming products and services (Sadorsky, Reference Sadorsky2013). The outcomes reported in column (1) of Table 4 reveal a noticeable increase after the city-county consolidation reform in energy consumption levels among the merged counties. This is consistent with the findings of Wang and Yang (Reference Wang and Yang2019), who show that urbanization can significantly increase energy intensity at certain stages of economic development, thereby negatively affecting energy efficiency. Our results suggest that city-county consolidation may induce an energy-intensive urbanization effect.

Table 4.

Mechanism analysis of city-county consolidation

Notes: The values enclosed in parentheses indicate county-level clustered standard errors.