Abstract
Background:
The green low-carbon transition is crucial not only for mitigating environmental challenges but also for reshaping residents’ consumption patterns.
Methods:
This study evaluates the impact of the urban low-carbon transition, specifically the Low-Carbon City Pilot (LCCP) policy, on the scale and structure of residents’ consumption. Leveraging the implementation of China’s LCCP policy as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) model and analyze panel data from 281 prefecture-level cities spanning 2007 to 2022.
Results:
The findings indicate that the LCCP policy significantly expands the scale and improves the structure of residents’ consumption. These results withstand a series of rigorous robustness checks. Heterogeneity analysis further reveals that the policy’s impacts on residents’ consumption scale and structure vary across different consumption tiers and regions.
Discussion:
Mechanism analysis suggests that the LCCP policy affects consumption primarily by increasing the supply of low-carbon products and strengthening residents’ low-carbon consumption awareness.
1 Introduction
Against the dual backdrop of global climate governance and the reorientation of economic growth drivers, the nexus between household consumption and low-carbon transition has emerged as a research topic of both theoretical significance and policy urgency. On the one hand, China’s economic growth is undergoing a structural shift from investment-driven to consumption-driven dynamics; understanding the determinants and evolving patterns of household consumption is thus critical for assessing the resilience of China’s economic growth and its trajectory of structural transformation. On the other hand, in response to climate change and in pursuit of green and low-carbon development, China has implemented the Low-Carbon City Pilot (LCCP) policy in three batches since 2010, promoting low-carbon transition at the city level through industrial restructuring, energy efficiency improvements, and the diffusion of green technologies (). Nevertheless, a fundamental question remains unresolved: how has this environmental policy, with its far-reaching socio-economic implications, shaped the scale and quality of household consumption?
The determinants of household consumption have long been a central topic in economic research. From the perspective of income distribution, the existing literature has identified the structural roots of China’s under-consumption: income inequality curbs consumption through status-seeking motives (), while the increasing burden of rigid expenditures such as housing, education, and healthcare has led to a surge in precautionary saving (). From the perspective of institutional environment, inadequate social security and labor market uncertainty are institutional drivers of high saving rates (), whereas the expansion of public service provision can effectively unlock consumption potential (Wang et al., 2025). However, these studies all treat environmental regulation as exogenously given, overlooking its potential role as a novel determinant of consumption. Although recent studies have begun to explore this nexus (), this emerging field remains in its infancy, with research on the quality dimension of consumption being particularly scarce. Incorporating environmental policy into the analytical framework of household consumption is both a theoretical imperative and a practical necessity for understanding the new characteristics of consumption behavior in the low-carbon era.
The LCCP policy provides an ideal quasi-natural experimental setting for addressing the above question. Its staggered rollout across multiple batches generates exogenous policy shocks, enabling this paper to identify the policy effects using a multi-period DID model. However, the existing LCCP policy evaluation literature has focused predominantly on emission reduction effects (; ) and green technology innovation (), among other dimensions. Studies examining how the LCCP policy affects residents’ economic welfare—particularly its impact on the scale and structure of residents’ consumption—remain relatively scarce.
Using panel data from 281 prefecture-level cities in China over the period 2007–2022, this paper employs a multi-period DID model to empirically examine the impact of the LCCP policy on household consumption and to explore its underlying mechanisms. This paper offers three potential contributions. At the theoretical level, it integrates environmental policy into the analytical framework of residents’ consumption determinants, thereby bridging consumption economics and environmental policy evaluation. At the empirical level, it is the first to simultaneously examine the differential effects of the LCCP policy on both the scale and structure of residents’ consumption, filling a gap in the existing policy evaluation literature. At the policy level, the findings provide decision-relevant insights for the design of differentiated low-carbon policies.
The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and presents the theoretical analysis, based on which we develop our research hypotheses. Section 3 describes the research design, including the data sources, variable construction, and econometric model specification. Section 4 reports the baseline regression results and robustness checks. Section 5 presents the heterogeneity analysis. Section 6 examines the underlying mechanisms. Section 7 concludes and discusses policy implications.
2 Review and theoretical analysis
2.1 Literature review
2.1.1 Research progress on the impact of green and low-carbon transition development
The primary research domains concerning the impacts of low-carbon transition development encompass energy low-carbon transition (; ), enterprise development (Zhao et al., 2023; ), individual behavior (; ), and economic growth (Zheng et al., 2021; ).
Academic research has focused on the impact of the green low-carbon transition on carbon emissions and energy efficiency. studied the impact of green patents, energy use and R&D on carbon emissions in the context of the LCCP policy in China based on panel data from 118 cities in China from 2003 to 2020, pointing out that green pa-tents and green technologies are conducive to sustainable development. examined the impact of the low-carbon city pilot policy on urban ecological resilience, pointing out that the policy enhances public awareness of environmental protection, reduces urban carbon emissions, and simultaneously improves urban ecological resilience.
In terms of enterprise development, scholars have mainly studied the impact of low-carbon transition development on enterprise innovation and low-carbon development. examined the impact of environmental and social sustainability on Japanese small and medium-sized enterprises using a qualitative case study methodology, which showed that the negative environmental and social impacts of SMEs can be significantly reduced if sustainable development initiatives and practices are integrated into their business activities from the outset. used data from A-share listed firms from 2010 to 2016 to investigate the effect of low-carbon city construction on the carbon reduction performance of enterprises using a double difference model. According to the study, corporate carbon reduction performance is enhanced by low-carbon city construction.
Research on individual behavioral responses to low-carbon transitions has primarily focused personal decision-making. For instance, used a survey methodology to explore how low-carbon initiatives shape future travel intentions. Their findings indicate that even individuals highly concerned about climate change often anticipate travel patterns that perpetuate high-carbon behaviors. In an-other study, leveraged LCCP policy as a quasi-natural experiment, applying a staggered DID approach. Their results demonstrated that low-carbon city development significantly increased public transit ridership and trips per capita.
Research on the socio-economic impacts of low-carbon transitions has extensively covered environmental quality and green economic growth. Empirically, assessed the effectiveness of low-carbon transport policies using Austria as a case study. Their analysis quantified the outcomes of policy packages and revealed important interactions between individual measures. The study demonstrated that a balanced policy mix can successfully decarbonize passenger transport while mitigating its negative externalities, without compromising economic welfare. Yuan and Wang (2026) constructed a theoretical model to examine the relationship between environmental protection and economic development. Their findings show that the low-carbon pilot city policy significantly improves both urban per capita GDP and air quality.
2.1.2 Progress in the study of residential consumption
The low-carbon transition, as a pivotal national strategy to combat environmental degradation, underscores the intrinsic link between environmental governance and consumption behaviors. This relationship was formally conceptualized with introduction of “green consumption,” a concept that has since evolved under various environmental regulations into related ideas like low-carbon and sustainable consumption. Contemporary research has empirically investigated the drivers of such behavior. For instance, found that green advertising and social norm interventions effectively promote low-carbon consumption. Similarly, , using structural equation modeling on survey data, demonstrated that publicity, education, and environmental values significantly foster green consumption behavior. The role of government is also critical; Yang et al. (2023) incorporated regret theory and environmental values into a multi-agent model, revealing that without regulatory impetus, residents lack strong incentives for green consumption. Beyond social and policy factors, macroeconomic conditions matter. , analyzing Chinese data from 2002 to 2021, showed that while economic policy uncertainty (EPU) generally suppresses urban consumption, the disruptive effects associated with natural resource development can paradoxically stimulate it.
2.1.3 Research gap
Through a literature review, it is found that current research on low-carbon transition development and residents’ consumption is relatively abundant, which lays a foundation for further in-depth study. However, there remain significant gaps and room for expansion in existing research: First, there are limitations in research perspectives. Most existing studies focus separately on the ecological benefits, corporate benefits, and the like of low-carbon transition, with few integrating low-carbon city pilot policies and overall residents’ consumption levels into a unified analytical framework. Second, there is a lack of targeted research. Few studies focus on the LCCP policy as a quasi-natural experiment to identify the net effect of policy shocks on residents’ consumption levels. To address these gaps, this paper takes 281 prefecture-level cities in China as research samples and employs a progressive DID model to systematically and empirically test the impact effects, transmission mechanisms, and heterogeneity characteristics of the LCCP policy on residents’ consumption levels, thereby filling the gaps in existing research and enriching the relevant findings on low-carbon transition and residents’ consumption.
2.2 Theoretical analysis
2.2.1 Supply-side analysis
Upon the enactment of the LCCP policy, pilot cities tend to adopt heterogeneous policy tools for low-carbon urban development, including mandatory emission reduction standards for high-polluting enterprises and market-based incentives such as green subsidies and carbon taxes (Yue et al., 2022). Enterprises characterized by high pollution, high emissions, and high energy consumption are thus incentivized to adjust production processes, upgrade manufacturing technologies, and reduce carbon emissions. To comply with these mandates, firms usually increase R&D investments in low-carbon technologies, which inevitably elevates production costs—especially for monopolistic or resource-intensive firms that lack market competition, who often pass these costs to consumers via higher product prices (). If consumer income remains constant, such price hikes will increase household expenditure burdens, thereby reducing their overall consumption levels.
Simultaneously, the LCCP policy stimulates firms’ green innovation through market-oriented mechanisms (Yue et al., 2022). Driven by technological advancement, firms can develop high-quality, diversified, and environmentally sustainable products, which better align with consumers’ demands for individuality and pro-environmental behavior—consistent with the theory of planned behavior that highlights attitude and perceived behavioral control in consumption decisions. These products thus significantly stimulate consumers’ purchase intentions (). Specifically, firms can showcase the variety of eco-friendly products through targeted advertising, which partially promotes consumers’ personalized consumption. In turn, the availability of “personalized, diversified, and high-quality” green products contributes to the optimization of local residents’ consumption patterns and the improvement of living standards.
2.2.2 Demand-side analysis
First, the Report of the 19th National Congress of the Communist Party of China states that the principal contradiction in Chinese society has evolved into the gap between the people’s growing aspirations for a better life and unbalanced and inadequate development. Correspondingly, the public’s demand for high-quality consumption has increased. Low-carbon pilot cities guide enterprises to adopt sustainable and low-carbon production models through subsidies for green industries and incentives for low-carbon technology R&D. These cities also provide high-quality, diversified, and refined goods and services. Such initiatives more accurately align with residents’ aspirations for a better life (), thereby expanding the scale of residents’ consumption and optimizing consumption structure.
Second, low-carbon pilot cities issue additional laws and regulations related to “low-carbon emission reduction” and conduct targeted publicity and educational campaigns to foster a new social consumption culture centered on “low-carbon consumption.” This consumption culture can partially influence residents’ knowledge of low-carbon consumption through the transmission of social norms (). As residents’ awareness of low-carbon consumption improves, they recognize the added value of low-carbon products and are willing to pay a premium for them. Conversely, if residents have weak awareness of low-carbon consumption and their consumption behaviors contradict the mainstream social low-carbon culture, they may experience group pressure in consumption scenarios. Consistent with the Social Norms Theory, this pressure ultimately prompts them to adjust or restrict their consumption behaviors.
Third, low-carbon pilot cities impose stricter regulations on “high-pollution, high-emission, and high-energy-consumption” enterprises, requiring them to upgrade production processes to reduce carbon emissions. This inevitably increases enterprises’ production costs, which in turn leads to higher prices of related products and partially restrains residents’ consumption. Meanwhile, the government provides direct subsidies to consumers who buy low-carbon products. By reducing consumption costs, these subsidies partially offset the restrictive effect of price increases, thereby providing overall support for consumption.
In summary, the LCCP policy simultaneously exerts both suppressing and promoting effects on residents’ consumption levels (Figure 1). The net effect on consumption outcomes is the result of the interplay between these two mechanisms, making it theoretically impossible to predetermine a single positive or negative direction. In terms of the pathways of influence, the upgrading of low-carbon product supply and the enhancement of residents’ low-carbon consumption awareness serve as the core positive transmission channels through which the policy affects residents’ consumption. Accordingly, this paper proposes the following research hypotheses:
FIGURE 1
H1The LCCP policy has a significant net effect on residents’ consumption levels.
H2The LCCP policy positively promotes residents’ consumption levels through two mediating pathways: the upgrading of low-carbon product supply and the enhancement of residents’ low-carbon consumption awareness.
3 Research design
3.1 Policy background
China announced three batches of low-carbon pilot cities in 2010, 2012, and 2017, respectively. The first batch comprised five provinces and eight cities; the second batch included 1 province and 28 cities; and the third batch covered 41 cities and 4 districts/counties. These pilot regions span a broad spectrum of geographic areas, development stages, resource endowments, and institutional foundations. These pilot regions vary significantly in their developmental stages, resource endowments, and operational foundations, providing a rich context for policy experimentation. In compliance with the pro-gram’s mandates, these cities have pursued significant initiatives in low-carbon planning, industrial development, and the promotion of green lifestyles. A key strategy has been to enhance residents’ low-carbon awareness through widespread publicity, thematic campaigns, and carbon-inclusive mechanisms, thereby encouraging the adoption of green consumption practices. Concurrently, the pilot cities have actively fostered a green industrial structure by developing the digital economy and phasing out traditional energy-intensive industries. This dual approach not only increases the market supply of low-carbon products but also effectively promotes the upgrading of residents’ consumption patterns. Consequently, the LCCP policy is positioned to exert a substantial influence on the enhancement of residents’ consumption.
3.2 Model design
China has released three batches of national low-carbon city pilot lists. This exogenous policy shock has altered the production activities and living standards of local cities, which in turn affected residents’ consumption—thereby creating a valid quasi-natural experimental setting for empirical research. Policy implementation may induce two key variations: (1) changes in residents’ consumption within the same pilot city before and after the policy rollout, and (2) contemporaneous differences in residents’ consumption between pilot and non-pilot cities. These variations enable us to identify the policy’s impact on residents’ consumption levels. Accordingly, this study employs a multi-period DID model to evaluate the effect of low-carbon transition on residents’ consumption levels. The model specification is as follows:
In Equation 1, the explanatory variable denotes the level of residents’ consumption of city in year . The explanatory variable represents a dummy variable for whether city becomes a pilot city in year . The regression coefficient represents the effect of low-carbon transformational development on residents’ consumption. represents a set of control variables that affect the level of residents’ consumption. is a city fixed effect, is a year fixed effect, and is a random error term.
3.3 Variable selection
3.3.1 Dependent variables
The dependent variables in this study are residents’ consumption level, which is analyzed through the dual dimensions of scale and structure, following the approach of . The scale of consumption () is proxied by the logarithm of the total retail sales of consumer goods. The consumption structure () is measured by the Engel coefficient, defined as the share of food, tobacco, and alcohol expenditure in total consumption expenditure. Following the UN Food and Agriculture Organization’s classification, which sets 40% as the threshold between moderate prosperity and relative affluence, this paper treats the Engel coefficient as an inverse indicator of consumption structure: a decline in the coefficient signals an upgrading of consumption structure, while an increase signals a downgrade. This interpretation presumes that changes in non-food expenditure genuinely reflect improvements in living standards. Notably, a “passive decline” in the Engel coefficient driven by rigid expenditures such as housing and healthcare—which crowd out food consumption—does not qualify as consumption upgrading under this framework.
3.3.2 Independent variable
The independent variable is a binary indicator for low-carbon pilot city designation (). It equals 1 for prefecture-level cities in the year they are designated as pilot cities and all subsequent years, and 0 otherwise. A key clarification is that when an entire province is designated as a pilot, all its prefecture-level cities are correspondingly coded as pilot cities. For cities designated multiple times, the earliest date is used as the policy onset.
3.3.3 Control variables
Referring to studies such as and , this paper selects the following five variables as control variables affecting the level of residents’ consumption: (1) the level of residents’ income (), which is expressed as the average of the sum of per capita disposable income of urban residents and per capita net income of rural households, taking logarithms; (2) governmental intervention (), expressed as the share of local general budget expenditures in local GDP; (3) the level of human capital (), expressed as the share of the number of students enrolled in higher education and the total population at the end of the year, in logarithmic terms; and (4) the level of regional economic development (), expressed as the per capita regional GDP, in logarithmic terms.
3.3.4 Sample and data sources
This study examines the effect of low-carbon transition on residents’ consumption levels, using a research period of 2007–2022 and a sample of 281 Chinese cities. Among these, 122 are low-carbon pilot cities (Table 1) and 159 are non-pilot cities. The data used in this study are sourced from the 2008–2023 China Urban Statistical Yearbook, statistical yearbooks of individual prefecture-level cities, the China Emission Accounts and Datasets (CEADs), and the official website of the State Intellectual Property Office of China. Missing values are imputed via interpolation. All variables measured in monetary terms are adjusted to 2007 constant prices to eliminate the impact of inflation. Descriptive statistical analysis results are presented in Table 2.
TABLE 1
| Batch | Year | Number of pilot cities | Eastern | Central | Western |
|---|---|---|---|---|---|
| First batch | 2010 | 71 | 39 | 13 | 19 |
| Second batch | 2012 | 24 | 14 | 4 | 6 |
| Third batch | 2017 | 27 | 9 | 11 | 5 |
| Total | — | 122 | 62 | 28 | 30 |
Composition of low-carbon pilot cities.
TABLE 2
| Variable types | Variable names | Variable symbols | Mean | Sd | Min | Max | Obs |
|---|---|---|---|---|---|---|---|
| Dependent variables | The scale of residents’ consumption | 15.431 | 1.121 | 11.946 | 19.013 | 4496 | |
| The structure of residents’ consumption | 0.345 | 0.073 | 0.110 | 0.873 | 4496 | ||
| Independent variable | Low-carbon pilot city dummy variable | 0.295 | 0.456 | 0 | 1.000 | 4496 | |
| Control variables | The level of residents’ income | 9.791 | 0.483 | 8.440 | 11.194 | 4496 | |
| Governmental intervention | 0.192 | 0.118 | 0.003 | 2.267 | 4496 | ||
| The level of human capital | 0.018 | 0.024 | 0 | 0.175 | 4496 | ||
| The level of regional economic development | 10.599 | 0.676 | 8.131 | 12.457 | 4496 |
Description of variables and descriptive statistics.
4 Results
4.1 Analysis of baseline results
Table 3 presents the empirical results of the baseline regression analysis examining the impact of the LCCP policy on both the scale and structure of residents’ consumption. Columns (1) and (3) of Table 3 focus just on the independent variable of low-carbon pilot cities, while Columns (2) and (4) incorporate all control variables inside the regression model.
TABLE 3
| Variables | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| 0.374*** | 0.095*** | −0.009*** | −0.004* | |
| (0.027) | (0.011) | (0.004) | (0.004) | |
| | 2.501*** | | −0.012*** | |
| | (0.063) | | (0.004) | |
| | −2.185** | | 0.0130** | |
| | (0.899) | | (0.006) | |
| | 11.921*** | | 0.128** | |
| | (0.782) | | (0.056) | |
| | −0.468*** | | −0.025*** | |
| | (0.101) | | (0.003) | |
| _cons | 15.321*** | −3.927** | 0.348*** | 0.729*** |
| (0.010) | (1.602) | (0.001) | (0.024) | |
| City fixed | Yes | Yes | Yes | Yes |
| Year fixed | Yes | Yes | Yes | Yes |
| N | 4496 | 4496 | 4496 | 4496 |
| Overall R2 | 0.078 | 0.541 | 0.011 | 0.081 |
| Between R2 | 0.911 | 0.953 | 0.814 | 0.847 |
| Within R2 | 0.026 | 0.495 | 0.003 | 0.052 |
Baseline results.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses. Three types of R2 are provided for each two-way fixed-effects panel specification: overall R2, between R2, and within R2. The high between R2. The consistently high between R2 across all four columns confirms that city-level covariates sufficiently capture long-term cross-city gaps for both outcome variables. Our DID, identification strategy exclusively exploits time-varying within-city fluctuations, which makes the within R2 the appropriate goodness-of-fit indicator for causal inference. Low overall R2 values stem from unobservable annual idiosyncratic shocks and saturated city and year fixed effects, so they cannot be regarded as the single criterion to evaluate the reliability of causal estimates. Almost all core explanatory variables reach statistical significance at the 1% or 5% level in all specifications, which validates stable and robust marginal effects.
The regression coefficients in Columns (1) and (2) demonstrate statistically significant positive values, indicating that, ceteris paribus, the implementation of the LCCP policy has a substantial positive effect on expanding the scale of residents’ consumption. The estimated coefficients in Columns (3) and (4) show statistically significant negative values, suggesting that the LCCP policy contributes to the optimization of residents’ consumption structure. This paper treats the Engel coefficient as an inverse indicator of consumption structure, following the UN Food and Agriculture Organization’s classification standards. A negative regression coefficient is interpreted as an improvement in consumption structure—provided that non-food consumption expenditure genuinely reflects an enhancement in living standards. Notably, a “passive decline” in the Engel coefficient driven by rigid expenditures such as housing and healthcare does not fall within this interpretation. Furthermore, the results in Columns (2) and (4) reveal that the control variables exert statistically significant influences on both the scale and structure of residents’ consumption, thereby confirming the appropriateness of their inclusion in the empirical model. Thus, Hypothesis 1 is validated.
4.2 Robustness check
4.2.1 Parallel trend test
The adoption of the multi-period DID model is justified by the need to satisfy the parallel trend assumption—a foundational premise of DID analysis. This assumption holds that, in the absence of the LCCP policy shock, the trends in residents’ consumption scale and consumption structure should be parallel between the pilot city group and the non-pilot city group.
To verify this assumption, this study employs the testing approach developed by for parallel trend analysis. Based on this framework and the study’s research objectives, the following model is constructed for the test:
The meaning of the relevant variables in Equation 2 is the same as Equation 1. mainly responds to the difference in the scale and the structure of residents’ consumption in pilot and non-pilot cities in year of the LCCP policy, which is the coefficient of focus in this section. Due to the time lag of policy implementation, the policy effect is not evident in the short run, while an excessively long-time window is vulnerable to disturbances from other macroeconomic policies and external shocks. Therefore, this study sets the three period prior to the launch of the low-carbon city pilot policy as the reference period and conducts regression analysis using data covering 5 years after policy implementation. The corresponding regression results are presented in Figures 2, 3. Figures 2, 3 illustrate that prior to the introduction of the LCCP policy, there were no discernible systematic differences in the scale and structure of residents’ consumption between the pilot and non-pilot city groups, as their pre-policy trends remained similar. After the implementation of the LCCP policy, the parallel trend was broken, leading to a significant impact on both the scale and structure of residents’ consumption.
FIGURE 2
FIGURE 3
4.2.2 Placebo test
To bolster the credibility of the baseline findings, this paper conducts two types of placebo tests.
This paper conducts a temporal placebo test to validate the parallel trends assumption and rule out confounding pre-existing trends. It artificially advances the actual LCCP implementation dates by two and 3 years, constructing two fictitious policy variables, denoted as and, for incorporation into Equation 1. As presented in Table 4, the estimated coefficients for these placebo policies are statistically insignificant across specifications for both consumption scale (Columns 1–2) and consumption structure (Columns 3–4). These results confirm the absence of systematic differences between future pilot and non-pilot cities prior to the actual policy intervention, thereby demonstrating that the baseline model successfully passes the placebo test.
TABLE 4
| Variables | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| 2 years ahead | 3 years ahead | 2 years ahead | 3 years ahead | |
| 0.011 | | 0.004 | | |
| (0.014) | | (0.007) | | |
| | 0.0117 | | 0.000 | |
| | (0.017) | | (0.008) | |
| Control variables | Yes | Yes | Yes | Yes |
| _cons | 7.361*** | 7.357*** | 0.850*** | 0.842*** |
| (0.795) | (0.795) | (0.269) | (0.268) | |
| City fixed | Yes | Yes | Yes | Yes |
| Year fixed | Yes | Yes | Yes | Yes |
| N | 4496 | 4496 | 4496 | 4496 |
| R2 | 0.977 | 0.977 | 0.730 | 0.730 |
Placebo test results.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses.
Second, this paper employs a spatial placebo test following to ensure that the baseline results are not driven by random factors. In this test, 132 cities are randomly selected as pseudo pilot cities from the full sample, with the remainder serving as the control group, thus creating a pseudo-treatment variable. This variable is substituted into Equation 1, and the entire process is re-peated 1,000 times. The kernel density distributions of the resulting 1000 coefficient estimate for consumption scale and structure are plotted in Figures 4, 5, respectively. Both distributions are approximately normal and centered around zero, indicating that the actual estimated effects are unlikely to be attributable to unobserved random factors.
FIGURE 4
FIGURE 5
4.3 Endogeneity test
To address the potential endogeneity issue within the model, this paper utilizes the research of to identify the urban air circulation coefficient () as an instrumental variable, employing the two-stage least squares method for regression analysis. The selection of this instrumental variable is theoretically grounded in two compelling rationales: First, within a given threshold of aggregate pollutant emissions, the intensity of urban air circulation exhibits an inverse relationship with local pollutant concentration levels (). Specifically, stronger air circulation capacity leads to greater dispersion of atmospheric pollutants, consequently reducing their concentration. This atmospheric mechanism enhances the probability of a city meeting the environmental criteria for low-carbon pilot designation, thereby satisfying the relevance condition for instrumental variable selection. Second, the air circulation coefficient is predominantly determined by exogenous geographical and meteorological factors, including but not limited to a city’s topographic features, latitude, and pre-vailing wind patterns. These factors are inherently independent of urban economic activities, thus satisfying the exclusion restriction requirement for valid instrumental variables. For each Chinese city, the air circulation coefficient was calculated using ERA-Interim data. It is defined as the average ventilation coefficient of the four nearest grid units in the dataset, obtained by matching the latitude and longitude of the city.
The natural logarithm of the air ventilation coefficient is incorporated as the instrumental variable in the two-stage least squares (2SLS) estimation framework, with the corresponding regression results presented in Table 5. Column (1) in Table 5 reveals that the regression coefficients of the instrumental factors and the time interaction term are significant, signifying the relevance of the instrumental variables. Furthermore, the Cragg-Donald Wald F-statistic substantially exceeds the conventional threshold of 10, indicating the absence of weak instrument problems. The regression coefficients of the pilot policy are considerable, and the direction of influence aligns with the benchmark regression, demonstrating that the pilot policy continues to significantly affect residents’ consumption upgrading after addressing endogeneity.
TABLE 5
| Variables | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| 0.142*** | | | | |
| (0.000) | | | | |
| | 0.102*** | −0.005** | | |
| | (0.026) | (0.003) | | |
| | | | −0.025 | |
| | | | (0.018) | |
| Control variables | Yes | Yes | Yes | Yes |
| _cons | −0.006 | 2.815*** | 0.697*** | −1.899 |
| (0.008) | (0.247) | (0.023) | (1.418) | |
| Weak identification test | 467,978 | | ||
| (16.38) | | | | |
| City fixed | Yes | Yes | Yes | Yes |
| Year fixed | Yes | Yes | Yes | Yes |
| N | 4496 | 4496 | 4496 | 4496 |
| R2 | 0.996 | 0.564 | 0.082 | 0.730 |
Instrumental variable test and sample randomness test results.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses. The weak identification test uses the Cragg- Donald Wald-F, statistic, and 16.38 is the critical value at the 10% level of the Stock-Yogo test.
4.4 Sample randomization issue
Although the selection of low-carbon pilot cities by the government is non-random, the robustness of the DID estimates is supported by empirical evidence in this study. The parallel trend assumption is confirmed, as the pilot and non-pilot cities exhibited similar development paths prior to the policy’s implementation. Additionally, a placebo test indicates no significant bias from unobservable factors. Even under the concern that selection may have been correlated with pre-existing carbon emission levels, the established parallel trends suggest that such a factor does not account for the observed out-come. This evidence collectively strengthens the causal interpretation of the policy effect.
To further assess the randomness of the pilot selection process, this study examines the relationship between pre-existing city-level carbon dioxide emissions () and the selection into the low-carbon pilot program. The regression results, presented in Column (4) of Table 5, show that the coefficient for carbon emissions is statistically insignificant. This finding suggests that cities with different levels of pre-treatment emissions were equally likely to be selected, thereby providing empirical support for the quasi-random nature of the selection process.
5 Heterogeneity analysis
5.1 Consumption level heterogeneity
This study further investigates the heterogeneous effects of the LCCP policy, examining disparities across different groups based on residents’ consumption levels and consumption structure. Specifically, the full sample is divided into high versus low-consumption groups, and superior versus inferior consumption structure groups, using the respective sample means as the thresholds. The group-specific data are then substituted into Equation 1 for regression analysis. The results, presented in Table 6, reveal whether the policy impact differs significantly between these groups.
TABLE 6
| Variables | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| High | Low | Superior | Inferior | |
| 0.022* | 0.012 | 0.001 | −0.008*** | |
| (0.013) | (0.020) | (0.005) | (0.002) | |
| Control variables | Yes | Yes | Yes | Yes |
| _cons | 7.481*** | 7.924*** | 1.171*** | 0.390*** |
| (0.500) | (0.711) | (0.250) | (0.041) | |
| City fixed | Yes | Yes | Yes | Yes |
| Year fixed | Yes | Yes | Yes | Yes |
| N | 2256 | 2240 | 2160 | 2336 |
| R2 | 0.910 | 0.837 | 0.078 | 0.028 |
| Test for between-group differences | ||||
| | 76.33*** | 313.42*** | ||
Impact of different consumption levels.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses.
As shown in Columns (1) and (2) of Table 6, the LCCP policy has a significantly positive effect on the high-consumption group but an insignificant effect on the low-consumption group. This disparity suggests that the policy primarily stimulates consumption among those with greater financial capacity, while leaving the consumption levels of the low-consumption group largely unchanged. The between-group coefficient difference test results indicate that the impact of the LCCP policy on residential consumption scale exhibits significant heterogeneity across cities with different consumption levels.
A plausible explanation is that high-consumption households, being more sensitive to quality of life and new products, are more responsive to the new low-carbon goods and services introduced by the policy. In contrast, the consumption of the low-income group is likely con-strained by their fundamental budgetary limitations, making them less sensitive to such market-led upgrades in consumption variety.
Columns (3) and (4) of Table 6 show that the LCCP policy has a significantly negative effect on the group with an inferior consumption structure but an insignificant effect on the group with a superior structure. This suggests that the policy is effective in optimizing the consumption patterns of the former group, potentially by guiding expenditure away from traditional, high-carbon options. In contrast, the latter group’s consumption structure, likely already more advanced and potentially including low-carbon preferences prior to the policy, remained largely unaffected. The between-group coefficient difference test indicates that the impact of the LCCP policy on residential consumption structure differs significantly between the superior and inferior consumption groups. This divergence can be attributed to the fact that households with superior consumption structures typically have greater economic flexibility and may have already begun transitioning towards low-carbon consumption. Meanwhile, the policy likely induced a structural shift for the other group through increased availability of green products and supportive measures like government subsidies.
5.2 Regional heterogeneity
To investigate the regional heterogeneity in the effects of the LCCP policy on both the scale and structure of residential consumption, this paper augments the baseline model (1) by introducing interaction terms between regional dummy variables and the core explanatory variable. The estimation results are reported in Table 7.
TABLE 7
| Variables | (1) | (2) |
|---|---|---|
| (Western) | −0.066 | 0.033*** |
| (0.044) | (0.004) | |
| 0.213*** | −0.072*** | |
| (0.049) | (0.005) | |
| 0.227*** | 0.002 | |
| (0.059) | (0.006) | |
| Control variables | Yes | Yes |
| _cons | −3.594*** | 0.501*** |
| (0.579) | (0.055) | |
| City fixed | Yes | Yes |
| Year fixed | Yes | Yes |
| N | 4496 | 4496 |
| R2 | 0.497 | 0.123 |
| Test for between-group differences | ||
| Eastern vs. central | 0.07 | 236.23*** |
| Eastern vs. western | 18.88*** | 243.93*** |
| Central vs. western | 14.88*** | 0.18 |
Results of regional heterogeneity analysis.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses. Benchmark group: western region. Between-group difference test: Wald test.
As shown in Column (1) of Table 7, the LCCP policy exerts heterogeneous effects on residential consumption scale across regions. Specifically, the policy has no significant impact on consumption scale in the western region, while its effects on the eastern and central regions are significantly positive. The between-group difference tests indicate that the eastern–central difference is not statistically significant, suggesting that the policy’s consumption-promoting effects are primarily concentrated in the eastern and central regions and have yet to effectively transmit to the western region. A possible explanation is that the western region lags behind in economic development, with relatively lower per capita income and underdeveloped green consumption infrastructure, which hinders the transmission channels of the low-carbon policy and prevents policy dividends from fully translating into drivers of consumption growth. Admittedly, this explanation warrants further empirical verification.
Column (2) of Table 7 reveals that the LCCP policy differentially affects residential consumption structure across regions. Specifically, the policy has a significantly positive effect on consumption structure in the western region, indicating that the share of food expenditure among western residents has passively increased, representing a “downgrade” in consumption structure. In contrast, the policy exerts a significantly negative effect on consumption structure in the eastern region, as reflected in a marked decline in the Engel coefficient and an upgrading of consumption structure. The between-group difference tests indicate that the eastern–central and eastern–western differences are both highly significant, while the central–western difference is not, confirming the presence of pronounced regional heterogeneity in the policy effects on consumption structure.
Within the analytical framework of this study, the “downgrade” in consumption structure observed in the central and western regions corresponds to a relative contraction of non-food consumption space. A possible explanation is that these regions are in the mid-stage of industrialization, characterized by a relatively heavy industrial structure and greater resident sensitivity to price fluctuations in energy and basic consumer goods. The LCCP policy, through cost pass-through mechanisms, may raise the relative prices of necessities, thereby crowding out non-food consumption. In contrast, the eastern region benefits from a robust economic foundation, higher income levels, and relatively well-established green consumption ecosystems, which enable the policy to facilitate consumption structure upgrading. It should be emphasized that our discussion of “downgrade” is a terminological expression within the framework of using the Engel coefficient as an inverse indicator of consumption structure, rather than a direct normative value judgment on the welfare status of residents in the central and western regions.
6 Mechanism analysis
6.1 Heterogeneity of consumption levels
The LCCP policy can indeed influence the scale and structure of Chinese residents’ consumption to a certain extent. According to the theoretical analysis in the previous section, the LCCP policy can influence the consumption scale and structure of residents by changing the supply of low-carbon products and raising their awareness of low-carbon consumption. This paper refers to the study of and establishes the following model to verify the mechanism of the supply of low-carbon products and residents’ low-carbon consumption awareness. The model is as follows:
In Equation 3, is the mediating variable, including the supply of low-carbon products () and residents’ low-carbon consumption awareness (), and other variables have the same meaning as Equation 1.
To examine the “supply of low-carbon products” mechanism, this study analyzes how the policy spurs low-carbon technological innovation—a prerequisite for market supply. Following Yu et al. (2022) and , it employs city-level green patent counts as a proxy for such innovation. Although patents measure innovation output rather than final supply, theory and evidence suggest policy-induced innovation is a key driver of product availability (). Thus, by testing the policy’s effect on green patents, the analysis captures its role in incentivizing innovation, thereby laying the groundwork for expanded low-carbon product supply.
To investigate the “low-carbon consumption awareness” mechanism, this study focuses on residents’ attention to low-carbon information, a key precursor to awareness (Wu et al., 2022). Following , this paper uses “low-carbon” as the keyword to retrieve the annual average search frequency for each low-carbon pilot city from the Baidu Index, and employs its logarithmic value to quantify residents’ low-carbon consumption awareness. The Baidu Index, derived from massive search behavior data, captures continuous changes in public attention and has been widely adopted in economics to measure latent variables such as investor attention and environmental concern.
It should be noted that, as a single-platform data source, the Baidu Index may not fully capture public attention across all internet channels. Nevertheless, we adopt this indicator for three reasons. First, Baidu remains the most widely used search engine in China, and its search data still offer unique representativeness in characterizing spatiotemporal variations in public attention. Second, our identification strategy relies on the relative differences between pilot and non-pilot cities and the temporal variation around the policy shock, rather than the absolute level of public attention—this comparability helps mitigate concerns about platform coverage bias. Third, a substantial body of literature has employed the Baidu Index to measure environmental concern and green consumption (; ), validating its applicability. Due to data availability constraints (the Baidu Index has been available only since 2011), we merge the first batch of pilot cities (2010) with the second batch (2012) for empirical analysis.
6.2 Analysis of mediation effect results
As shown in Column (1) of Table 8, the LCCP policy exerts a significantly positive effect on the number of green patents. This finding indicates that the policy effectively promotes innovation in low-carbon technologies. According to the theory of innovation diffusion, technological innovation serves as a fundamental driver for the introduction and increased supply of new products. Therefore, the results support a transmission channel in which the LCCP policy stimulates low-carbon technological innovation, which in turn creates the conditions for an expanded market supply of low-carbon products, ultimately influencing residents’ consumption patterns. This interpretation is consistent with the view of , who also emphasize that policy-driven green innovation plays a key role in transforming the structure of the consumption market.
TABLE 8
| Variables | (1) | (2) |
|---|---|---|
| 0.057* | 0.058*** | |
| (0.033) | (0.018) | |
| Control variables | Yes | Yes |
| _cons | −31.712*** | −8.707*** |
| (0.730) | (0.482) | |
| City fixed | Yes | Yes |
| Year fixed | Yes | Yes |
| N | 4496 | 2810 |
| R2 | 0.624 | 0.470 |
Results of the mediation effect test.
*p < 0.1, **p < 0.05, ***p<0.01, Robust standard errors in parentheses.
As shown in Column (2) of Table 8, the LCCP policy significantly increased public attention to “low carbon,” as measured by the Baidu Index. This finding suggests that the policy implementation effectively enhanced residents’ attention to low-carbon information. Consumer behavior theory posits that attention is a key antecedent to attitude and behavioral change. Enhanced public attention can increase familiarity with and preference for low-carbon products, thereby potentially steering consumption decisions towards greener choices (Wei et al., 2023). Thus, the results support a transmission path in which the LCCP policy elevates public attention to low-carbon information, thereby shaping low-carbon consumption awareness and preference, and ultimately affecting the scale and structure of resident consumption. In summary, Hypothesis 2 is validated.
7 Conclusions and policy implications
7.1 Conclusions
This study empirically examines the effects, mechanisms, and heterogeneity of the LCCP policy on the scale and structure of residents’ consumption in China. Utilizing panel data from 281 prefecture-level cities over the period 2007–2022 and employing a multi-period DID model that treats the policy as a quasi-natural experiment, the analysis yields three primary findings. First, the LCCP policy significantly enhances the scale of residents’ consumption and optimizes its structure, a conclusion that remains robust after a series of tests. Second, notable heterogeneity exists in the policy effects. The impacts vary across groups with different consumption levels and display distinct regional patterns between the eastern, central, and western parts of China. Third, mechanism analyses indicate that the policy operates through two key channels: by increasing the supply of low-carbon products and by raising residents’ awareness of low-carbon consumption.
7.2 Policy implications
First, governments should set tiered green consumption incentives by household spending capacity to narrow unequal policy dividends. Table 6 shows the policy only lifts consumption scale for high-spending households and optimizes structure for groups with poor initial spending composition, while low-consumption residents gain no benefits. Authorities can launch premium green consumption scenarios and reward schemes for high-consumption groups nationwide. Targeted coupons for energy-saving appliances and cheap green food should be prioritized for low-income, structurally disadvantaged households to lower green consumption thresholds.
Second, pilot cities need customized carbon reduction targets instead of unified national standards. Table 7 confirms heterogeneous regional effects: eastern cities achieve both consumption growth and optimized Engel coefficients; central cities only see expanded consumption volume; western cities face stagnant spending and worsened consumption structure. Eastern areas may rely on market-driven green consumption under strict low-carbon industries. Central regions need cost buffer policies to avoid daily goods inflation crowding out developmental spending. Western cities should extend carbon transition schedules and prioritize green infrastructure before rigid carbon constraints.
Third, a two-tier fair transition compensation system shall ease regressive living costs. A cross-regional fiscal fund funded by eastern pilot surpluses transfers support to central and western areas. Region-specific subsidy schemes apply: western cities adopt universal daily necessity subsidies; central cities launch tiered green vouchers favoring low-consumption families; eastern areas shift subsidies from general living allowances to energy-efficient durable goods.
Fourth, differentiated low-carbon publicity shall match regional economic features and resident spending levels. Eastern campaigns target high-consumption groups to advocate sustainable lifestyles. Central publicity promotes affordable green daily goods to structurally disadvantaged residents. Western authorities shall fully publicize supportive compensation policies and popularize zero-cost energy-saving tips for low-consumption households, avoiding overpriced green product promotion.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
PC: Conceptualization, Data curation, Formal Analysis, Writing – original draft, Writing – review and editing. WX: Conceptualization, Data curation, Writing – original draft, Writing – review and editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
low-carbon city pilots, residents’ consumption scale, residents’ consumption structure, residents’ low-carbon consumption awareness, the supply of low-carbon products
Citation
Chen P and Xi W (2026) The impact of urban low-carbon transition development on residents’ consumption levels-evidence from China’s low carbon city pilot policy. Front. Environ. Sci. 14:1870340. doi: 10.3389/fenvs.2026.1870340
Received
01 May 2026
Revised
20 June 2026
Accepted
21 July 2026
Published
14 August 2026
Volume
14 - 2026
Edited by
Guifu Chen, Xiamen University, China
Reviewed by
Tong Zou, Xinzhou Normal University, China
Jiqiang Zhao, Zhejiang Agriculture and Forestry University, China
Updates
Copyright
© 2026 Chen and Xi.
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*Correspondence: Pian Chen, 1920220010@stu.nufe.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.