Will large-scale low-carbon guidance activities improve residents’ green living standards? Evidence from China

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Research article
Will large-scale low-carbon guidance activities improve residents’
green living standards? Evidence from China
Zhengyun Wei
a
, Liang Wan
b,*
, Qiaoqiao Zheng
c
, Zexian Chen
b
, Shanyong Wang
b
a
School of Economics, Hefei University of Technology, Hefei, 230009, China
b
School of Public Affairs, University of Science and Technology of China, Hefei, 230026, China
c
School of Management, University of Science and Technology of China, Hefei, 230026, China
ARTICLE INFO
Keywords:
Low-carbon guidance activities
Green lifestyle
Multi-period DID
Environmental awareness
Green technology innovation
ABSTRACT
In order to address the escalating climate change crisis and meet Net-Zero Emission targets, it is
necessary to form a widespread green lifestyle as soon as possible. Based on annual panel data at
the provincial level in China from 2008 to 2019, this paper systematically explores the impact of
large-scale guidance activities on residents’ green lifestyles by constructing a multi-period DID
model. The results show that the national low-carbon pilot signicantly improves the green living
standards of residents, and these results remain robust after various rigorous tests. In addition, the
results of heterogeneity analysis show that in areas with higher income levels and education
levels, the low-carbon pilot projects have a better impact on improving residents’ green lifestyles.
Furthermore, this paper reveals that residents’ environmental awareness and regional green
innovation levels signicantly and positively moderate the impact of the pilot projects on resi-
dents’ green lifestyles. This study expands our understanding of the mechanisms driving a green
lifestyle, offering valuable insights for governments aiming to promote widespread adoption of
green practices and formulate effective policies.
1. Introduction
Humanity’s sustainable development is confronted with increasingly severe challenges. The primary driver behind the escalating
occurrence of these extreme weather events is widely attributed to carbon emissions [1]. In response to this crisis, 140 countries have
either announced or are contemplating public commitments to adopt net-zero emission policies [2]. For instance, China has made a
solemn commitment to achieve carbon peaking before 2030 and carbon neutrality by 2060 as part of its contribution to global climate
change mitigation efforts. For a long time, carbon emissions at the individual level have not received sufcient attention. However, as
the endpoint of carbon consumption, families or individuals contribute the largest part of carbon emissions in this society. Currently,
the carbon emissions of Chinese residents have exceeded the total emissions by more than 45 % [3–5]. Therefore, to achieve the dual
carbon goals, it is imperative to harness the carbon reduction potential of hundreds of millions of individuals on both the lifestyle and
consumption fronts, which requires residents to achieve a green transformation of their lifestyles [6].
Green lifestyles entail incorporating eco-friendly behaviors into one’s daily practices [7]. Fundamentally, cultivating diverse green
lifestyles requires enhancing societal awareness and active engagement in ecological environmental protection, embracing green
development principles, and adopting a mindset of “moderation and judicious use.” This collective endeavor aims to advance green
* Corresponding author.
E-mail address: wanl001@ustc.edu.cn (L. Wan).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2024.e38665
Received 8 November 2023; Received in revised form 7 September 2024; Accepted 26 September 2024
Heliyon 10 (2024) e38665
Available online 28 September 2024
2405-8440/© 2024 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

development through social synergy [8]. Certain scholars have undertaken theoretical research concerning the conceptual connota-
tion, denition, and promotion methods of green lifestyles[9–13]. According to Zheng et al. (2023), a green lifestyle among residents
encompasses daily environmental protection behaviors, such as reducing energy consumption and using eco-friendly transportation
[14]. Steg and Vlek (2009) introduced a comprehensive framework for altering human behavior patterns through the examination of
pro-environmental behavior [15]. Lange and Dewitte (2019) organized the prevailing measurement methods of pro-environmental
behavior and delineated the limitations of various measurement approaches, it depending on the research questions, distinct mea-
surement models should be chosen [16]. While there is presently no standardized measurement method, scholars have questioned the
use of self-report forms.
Currently, some scholars have initiated exploration into guiding residents’ lifestyles, yet there is limited empirical evidence.
Scholars have examined inuencing factors of specic lifestyles through the lenses of behavioral psychology and behavioral economics
[6,17,18]. For instance, Sony and Ferguson (2017) discovered through a questionnaire survey that green lifestyle behaviors are pri-
marily motivated by self-interest and social altruism values [10]. Research by Kuai et al. (2022) has identied that heightened
environmental awareness positively inuences household energy conservation [19]. It is evident that scholars have conducted
extensive research from the perspectives of psychology and behavior. Nevertheless, there are currently limited studies on external
environmental policy factors, with a notably lack of research using causal paradigm.
Carbon emissions have traditionally been seen as a by-product of economic growth. Apart from depending on residents’ intrinsic
moral constraints to curtail emissions, it is imperative to leverage diverse inuencing factors on the demand side for altering residents’
lifestyles, particularly through government policy guidance to promote green and low-carbon living [2,8]. Particularly in developing
countries, policy decision-making tendencies play a crucial role in shaping the developmental trajectory of the entire society [20].
Recent studies, exemplied by Zhang and Zheng (2023) have started employing methods like constructing econometric models to
empirically investigate the inuence of environmental regulations on green lifestyles [8]. However, generally, there is a scarcity of
pertinent empirical research on the theme of green lifestyle. Existing empirical research frequently originates from various environ-
mental policy types (formal environmental and informal environmental regulations) [14] or specialized low-level research conducted
from the perspective of carbon policy, such as low-carbon city pilot programs [17]. The research perspective and content require
further enrichment.
In contrast to prior research centered on the perspective of environmental regulation or behavioral psychology, this study inno-
vatively opts to initiate its investigation from policy guidance activities directly inuencing residents’ behavior. It employs a causal
analysis method to examine the impact channels on residents’ green lifestyle, marking a somewhat novel approach (theoretical
analysis framework is shown in Fig. 1). Since its inception in 2011, the “Cool China-One Nationwide Low-Carbon Action Plan”
campaign has stood as a pivotal initiative, engaging the Chinese public in efforts to address climate change by promoting a low-carbon
lifestyle. It was highlighted in the white paper ‘China’s Policies and Actions on Climate Change’ and gained prominence when
showcased at the United Nations Climate Summit in Durban, South Africa [21,22]. Building on the above, this article focuses on the
large-scale promotion of residents’ low-carbon behavior guidance—an initiative pioneered in China. It explores its role in fostering the
development of residents’ green lifestyles, serving as a crucial reference for the examination of government policy decisions.
Utilizing panel data from 24 Chinese provinces from 2008 to 2019, this study quantitatively assesses the impact of policy pilots
programs on the adoption of green lifestyles. The results indicate that the large-scale policy guidance, such as the “National Low-
Carbon Action Plan”, signicantly advances the adoption of environmentally sustainable transformations in regional residents’ life-
styles. After eliminating potential effects from other policy interferences and conducting placebo tests, the conclusion remains valid.
Additionally, this study identies that the impact varies among regions with differing income or education levels. Environmental
awareness among residents and the regional innovation level positively contribute to regulatory mechanisms. This study makes several
contributions: rstly, it evaluates the impact of policy pilots on residents’ lifestyles using causal analysis methods, introducing a novel
perspective to lifestyle research; secondly, it quantitatively assesses residents’ lifestyle greening levels, offering a more objective
Fig. 1. Theoretical analysis framework.
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measurement compared to previous self-reports; thirdly, the study explores two regulatory mechanisms-environmental awareness and
regional innovation level-enhancing the impact framework. The ndings can assist policy makers to scientically formulate carbon
reduction policies at the residential level to achieve carbon emission targets (the research route is shown in Fig. 2).
The organization of this study is as follows: Section 2provides a detailed description of the study area and the data used in the
analysis. Section 3outlines the research design, detailing the methodology employed and how the variables are measured. Moving on
to Section 4, we evaluate the empirical results, which encompass benchmark model analysis, parallel trends tests, robustness tests, and
heterogeneity analysis. In Section 5, we conduct a further analysis to explore how environmental awareness and green innovation
serve as channels for promoting green lifestyles. Lastly, Section 6presents the conclusions drawn from our study, as well as the im-
plications of our ndings. Following this organizational structure, our study aims to comprehensively explore the impacts and
mechanisms of low-carbon behavior guidance activities, thereby contributing to a better understanding of how to effectively
encourage residents to embrace green lifestyles. The research ndings of this study can assist government departments in making more
informed strategic decisions. This can lead to the effective reduction of carbon emissions at the resident level through scientic
government management and social governance, thereby expediting the achievement of dual carbon goals.
2. Study area
2.1. Activity introduction
With the gradual popularization of the low-carbon economy, various aspects of production and daily life are increasingly inu-
encing the transition towards a comprehensive low-carbon lifestyle [23,24]. Recognizing the importance of individual behaviors in
contributing to carbon dioxide emissions and the need to guide the public to take practical actions to effectively address climate
change, the “Cool China” environmental protection activity was widely implemented in China. This initiative is hosted by the Publicity
and Education Center of the Ministry of Environmental Protection and the Environmental Protection Association of the United States
[22]. It is co-organized by the Green Travel Fund of the China Civil Promotion Association.
The national Low-Carbon Action Plan aims to actively promote low-carbon, green lifestyles and consumption patterns in selected
pilot provinces and cities [25]. Through activities targeting schools, communities, enterprises, and the general public, the plan aims to
encourage individuals and families to voluntarily reduce carbon dioxide emissions in their daily lives. The ultimate goal is to
encourage widespread participation and deliberate actions from the entire population, contributing to global climate change miti-
gation efforts and China’s carbon reduction goals. Overall, the “Cool China - Low-carbon Action Plan for All” serves as a signicant
commitment by China to promote environmental awareness and encourage sustainable practices on a national scale.
The implementation of the plan by ‘Cool China’ has proven to be an effective way to translate national policies into concrete actions
within families and communities. The initiative started in 2011, targeting 5 provinces and 8 cities. These pilot areas actively explored
and implemented various forms of low-carbon communities and lifestyles. In 2012, two more municipalities, Shanghai and Beijing,
joined the efforts to promote low-carbon advocacy among the public. The national low-carbon action plan has successfully carried out
a series of activities, both online and ofine, to engage and educate the public. Innovative online participation methods have been
Fig. 2. Research route.
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introduced, such as the concept of “carbon accounts” for households, aiming to involve core groups in the process. Ofine activities
include national low-carbon roadshows, which have attracted widespread public participation. The national low-carbon action plan
campaign has made signicant impact and achieved positive results. In 2011, it was even recognized as one of China’s top ten low-
carbon news events. This demonstrates the successful implementation of the initiative and its ability to generate awareness and ac-
tion towards a low-carbon future.
2.2. Data sources
Based on the above research background, the treated groups in this paper include the 5 provinces and 2 cities that launched the
pilot projects in 2010, as well as Beijing and Shanghai, which started their pilot projects in 2011. The control group comprises 15 other
provinces in China. To ensure the consistency of dimensions and minimize policy interference, cities other than those municipalities
were excluded from the pilot scope, as well as the provinces where the pilot cities were located. The resulting panel data covers a
period from 2008 to 2019 and includes a total of 24 Chinese provinces (including municipalities).
Data collection for this research was based on a variety of sources, such as the China Statistical Yearbook, China Environmental
Yearbook, statistics from the Ministry of Industry and Information Technology of China, the CNRDS, and statistical yearbooks from
each province. However, data for Tibet, Macao and Hong Kong were unavailable and therefore excluded from the analysis.
3. Research design
3.1. Muti-period DID regression
The DID method is widely used for evaluating the effects of relevant pilot programs [26,27]. For this study, by comparing the
experimental group affected by the guidance activity with the control group not affected by the guidance activity before and after the
pilot, at the same time, the confounding factors that do not change with time or cannot be observed are eliminated, and the real results
of the impact of the pilot activities are effectively separated, so as to obtain the net effect of the pilot program’s impact.
The pilot implementation of the “National Low-Carbon Action Plan” offers a robust quasi-natural experiment. Consequently, this
article employs the difference-in-difference method for conducting causal analysis. As the pilots are implemented in different years, we
construct the following multi-period DID model to estimate the impact of the large-scale low-carbon guidance activities on green
lifestyle of residents:
Green lifestyleit =β0+β1Activityit +β∑Zit +
μ
i+
ν
t+
ε
(1)
Where, Green lifestyleit is green lifestyle of residents in city i in year t. Activity
it
represents the core explanatory variable - the national
Low-carbon Action Plan, which is a dummy variable. Activity
it
=1 means that individual i is impacted by the pilot at time t. The
coefcient β1 holds signicance as it reects the net impact of the pilot, capturing our primary interest. Z
it
is the control variable that
changes with the individual and time, including regional development level, R&D investment, etc.
μ
i
is the xed effect of region,
ν
t
is
the xed effect of time, and
ε
is the random error term.
3.2. Variables
3.2.1. Independent variable
National Low-Carbon Action Plan (Activity). In this paper, the pilot of the National Low-Carbon Action Plan was used to evaluate
Fig. 3. The changes of green lifestyle levels between 2008 and 2019 in China.
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the impact of large-scale guidance activities, and the area affected by the guidance activities was the experimental group, which treat
=1, and the area not affected by the guidance activities was the control group, which treat =0. According to the inuence time of the
guiding activity, the dummy variable time =0 before the guiding activity is implemented, and time =1 after the guidance activities is
implemented. Activity
it
is the interaction item of treat and time, and Activity
it
=1 means that the experimental group has been
impacted by the guidance activities. Its estimated coefcient is the pure effect of the guidance activities shock.
3.2.2. Dependent variable
Green lifestyle of the residents (Green_lifestyle). Prior studies generally adopt questionnaires to measure residents’ green lifestyle
[28,29]. However, this could give rise to estimation biases because self-reports of residents may be biased from their actual lifestyle in
reality. In this paper, a proxy variable is developed to measure the green lifestyle of residents based on previous research. The selected
variables are organized into three dimensions, the rst dimension includes ‘water consumption per 10,000 people’ and ‘electricity
consumption per 10,000 people’. These variables act as reverse indicators, meaning that lower values indicate higher levels of energy
conservation. The second dimension includes ‘public transportation ridership per 10,000 people’, this variable reects residents’ travel
habits and whether they prioritize green behaviors [30,31]. The second dimension includes ‘new energy vehicles purchased per 10,000
people’. When residents choose to purchase, selecting new energy vehicles can effectively reect residents’ inclination towards green
products, and this inclination is well manifested in their daily lives [32,33]. By using these proxy variables, the paper aims to capture
the green lifestyle of residents. The higher value of the proxy variable, the greater intensity of green lifestyle of the residents. Fig. 3
presents the changes of green lifestyle levels from 2008 to 2019 in China. It shows a signicant enhancement of green lifestyle of
Chinese residents.
3.2.3. Moderating variables
Environmental awareness (Aware). Environmental awareness of the residents is measured by the number of local environmental
complaints. As an important environmental participation behavior of residents, environmental complaints can directly reect the
environmental awareness of residents [34]. Prior to 2016, environmental complaints were mainly made through traditional
communication methods such as telephone and letter. Since 2016, the “12369 Environmental Complaint Report” WeChat public
account operated by the Ministry of Environmental Protection has been added to residents’ petition channels. Therefore, to ensure the
uniformity of dimensions, we normalize the environmental petition data before including in the regression model.
Green technology innovation (Tech). Measured in this study by calculating the ratio of the number of green innovation patents in a
focus city to the total number of green innovation patents nationwide [35]. Patents were searched and screened using resources from
the State Intellectual Property Ofce (SIPO) and Google Patents. The data covers both patent applications and authorizations related to
green technologies. Due to the potentially lengthy development cycle of patents, which can result in lagging effects, the study in-
corporates a one-period lag in the analysis. This means that the results of the green patent data are used with a one-year delay to
account for any potential time lags in the impact of green technology innovation.
3.2.4. Control variables
According to the discussions and recommendations of Angrist and Pischke (2009), a comprehensive consideration of data avail-
ability is taken into account [36], the goal is to ensure that each control variable is as exogenous as possible. The following control
variables are included in the variable set Z: 1. GDP: Measured by gross regional product, reects the level of development in a region. 2.
R&D intensity (RD): Measuring the ratio of R&D investment to GDP. 3. FDI: Represented by the ratio of foreign investments to GDP. 4.
Industrial structure (IS): Measured by the proportion of the secondary industry in GDP. 5. Income level (IL): This variable reects the
per capita disposable income of residents. 6. Educational level (EL): Measured by the number of years of schooling per person.7. Age
structure (AS): This variable indicates the proportion of the population aged 15–64 years old relative to the total number of residents.
To address heteroscedasticity, certain control variables were logarithmically transformed.
Table 1 presents the descriptive statistics of above variables.
Table 1
Descriptive statistics.
Variable Obs Mean Sd Min Max
Green_lifestyle 288 0.1478 0.1349 0.0501 0.9124
Policy 288 0.2431 0.4297 0.0000 1.0000
Aware 288 0.1068 0.1471 0.0005 0.9931
Tech 288 0.0126 0.0196 0.0001 0.1535
GDP 288 9.5231 0.9078 6.8685 11.5092
RD 288 1.5882 1.1755 0.2227 6.3147
FDI 288 0.0580 0.0722 0.0077 0.6624
IS 288 0.4452 0.0925 0.1616 0.6148
IL 288 9.8217 0.4177 8.8469 11.1482
EL 288 9.0814 1.0198 6.9018 12.7177
AS 288 0.6560 0.0845 0.4961 0.9139
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4. Empirical results
4.1. Baseline regression results
According to Equation (1), we conducted an estimation of the impact of the pilot. The results are presented in Table 2, with column
(1) displaying the ndings after accounting for both province and year xed effects. In column (2), control variables are added on top
of the specications in column (1). The results indicate that the coefcient of the interaction term is positive and statistically signicant
at the 5 % level. This suggests that the activity has a positive impact on the adoption of green lifestyles. In other words, the national
low-carbon action plan has the potential to guide residents towards switching to greener and more environmentally-friendly lifestyles
and consumption patterns. These ndings imply that the national low-carbon action plan play a crucial role in promoting and
encouraging residents to embrace green and low-carbon practices. Prior research has demonstrated that a community’s adoption of a
green lifestyle may prompt other individuals to emulate it. The nationwide low-carbon initiative, by implementing demonstration and
promotional activities such as green schools and green communities in pilot areas, encourages conscious imitation among neighboring
residents. This, in turn, facilitates the breaking of traditional living habits among the entire community, fostering the development of
new green lifestyle habits [37]. Additionally, the conclusions of this study also highlight the effectiveness of non-coercive policy
measures. Coercive measures have long been perceived to have better implementation and enforcement capabilities [38]. However,
the intensity of coercive policies is challenging to gauge, often leading to individual dissatisfaction, and the translation of standards to
the individual level is difcult to determine. Non-coercive policies address certain shortcomings of coercive measures by stimulating
individual initiative, thereby effectively guiding resident behavior as intended.
4.2. Parallel trend test
To validate the effectiveness of baseline regression results, we conduct a parallel trend test. In this paper, we used the event
research method proposed by Jacobson et al. (1992) [39]. Due to the inconsistent pilot time in the pilot areas, when generating time
dummy variables, the guidance activities of each experimental group were used as the base period to construct the following model for
parallel trend testing.
Green lifestyleit =β0
ʹ+∑
−1
j=− 4
βjdidj+βjdid0+∑
5
l=1
βldidl+
μ
i+λt+β∑Zit +
ε
it (2)
Different from the benchmark model, in Equation (2) we add a set of dummy variables did j, did0, did l. did j is the jth year before
the implementation of the activity, and the implementation year of the activity is taken as the base period. For the jth year before the
implementation of the guidance activity, the value of did j is 1, otherwise it is 0; did l is the lth year after the implementation of the
activity, and for the lth year before the implementation of the guidance activity, the value of the did l is 1, otherwise it is 0. At the same
time, in order to avoid the interference of multicollinearity, drop the low-carbon guidance activity which was implemented in the
current period, and a parallel trend chart was drawn according to the results, the results are shown in Fig. 4.
Table 2
Baseline regression results.
(1) (2)
Activity
it
0.114** 0.087**
(0.054) (0.042)
GDP 0.070
(0.098)
RD 0.063
(0.075)
FDI 0.195
(0.342)
IS −0.079
(0.403)
IL 0.059
(0.134)
EL 0.075
(0.057)
AS −0.427
(0.412)
_cons 0.120*** −1.603
(0.013) (1.543)
Year xed effects Yes Yes
Regional xed effect Yes Yes
Observations 288 288
R-squared 0.675 0.707
***p <.01, **p <.05, *p <.1. Standard errors are in parentheses.
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4.3. Robustness test
4.3.1. Controlling potential confounding factors
According to the above, when measuring green lifestyle, we selected ‘new energy vehicle purchases per 10,000 people’ as one of the
measurement indicators. However, since 2009, China’s new energy vehicle subsidy policy has gradually been launched, which may
have an impact on the green lifestyle of residents [40]. Therefore, in order to prevent the new energy subsidy policy from interfering
with the test results, we generated a new energy vehicle subsidy policy dummy variable and incorporated it into the benchmark model:
Green lifestyleit =β0
ʹʹ +β1
ʹʹActivityit +β2
ʹʹGreencarit +β∑Zit +
μ
i+
ν
t+
ε
(3)
In the above formula, Green carit is the new energy vehicle subsidy pilot, when Green carit =1 means that province i is affected by the
new energy vehicle subsidy pilot at t time, if it is not affected by the subsidy policy, it is 0.
In Table 3, it can be seen that after the introduction of the new energy vehicle subsidy pilot policy, the national low-carbon action
pilot still has a positive and signicant impact on the green lifestyle of residents, which consolidates the research conclusions of this
paper. At the same time, we can see that the coefcient of the new energy vehicle subsidy pilot is not signicant. This may be because
subsidies to a certain extent promote residents who have the intention to buy new energy vehicles to buy [41]. For residents who do not
have green purchase intentions, the guidance of the subsidy policy may still not eliminate the doubts caused by the shortcomings of
new energy vehicles.
4.3.2. Placebo test
Since the model used in this paper is multi-period DID, the sample period needs to be randomly selected for each province as the
guidance activity time when performing the placebo test. Specically, in the placebo test, a certain year from 2008 to 2019 was
randomly selected for the 9 experimental groups and 15 control groups as the guidance activity time. Each time 9 provinces were
randomly selected as the experimental group, and the pilot time of the guidance activity was randomly given, a total of 500 times were
selected, and 500 sets of policy dummy variables were obtained. The kernel density plot of the test results and its p-value distribution
are presented in Fig. 5. The results show that the coefcients are concentrated around 0, and the p-values are generally greater than
0.1. At the same time, the estimated coefcients of placebo test results differed signicantly from the estimates of actual activity.
Therefore, it can be seen that the test results of the original guidance activity are not random, and the original results are robust.
4.4. Heterogeneity analyses
Residents are the main practitioners as well as the main beneciaries of green life style. However, the above analyses based on the
overall sample may ignore some potential heterogeneities. The differences in individual and social characteristics across residents may
lead to the heterogeneity of the pilot on the green lifestyle of residents. To this end, this paper will conduct heterogeneity analyses from
two aspects, i.e., income level and education level.
Fig. 4. Parallel trend test results.
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4.4.1. Heterogeneity analysis of income level
Scholars have different opinions on the inuences of income level. Some scholars suggest that people with lower income may use
less energy [42], making them more inclined to engage in energy-saving practices. However, with the improvement of the overall
economic level of the society, people’s understanding of the green lifestyle is transforming from reducing energy use to improving
energy use efciency, which means that a certain amount of capital accumulation is needed to realize the transformation and
upgrading of green technology [43]. However, other scholars indicate that income is inversely proportional to energy conservation.
For example, Sardianou (2007) argues that with the increase of household income, people are more inclined to save energy, primarily
because they are able to afford investments that improve energy efciency [9].
In this study, considering that residents of different income levels may choose different green lifestyles under the inuence of pilot
Table 3
Analysis results with other policy interference.
(1)
Activity 0.083**
(0.04)
Green_car −0.049
(0.039)
Control variable Yes
Year xed effects Yes
Provincial xed effect Yes
Observations 288
R-squared 0.715
***p < .01, **p < .05, *p < .1. Standard errors are in
parentheses.
Fig. 5. Distribution of coefcient estimates after random processing.
Note: The X-axis is the estimated coefcient of 500 randomly generated low-carbon guidance activities. The circle is the P value of the estimated
coefcient, the solid line is the kernel density distribution of the estimated coefcient; the vertical line is the estimated coefcient of the activity.
Table 4
Heterogeneity analyses.
Income Education
(1) (2) (3) (4)
High level Low level High level Low level
Activity 0.051* −0.007 0.065* 0.007
(0.03) (0.01) (0.035) (0.009)
_cons −6.111*** 0.922 −6.454*** 1.576**
(1.623) (0.804) (1.832) (0.646)
Control variable Yes Yes Yes Yes
Year xed effects Yes Yes Yes Yes
Provincial xed effect Yes Yes Yes Yes
Observations 144 144 144 144
R-squared 0.874 0.683 0.806 0.811
***p <.01, **p <.05, *p <.1. Standard errors are in parentheses.
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guidance activities, the samples are divided into two groups according to residents’ income level, and difference-difference test is
accordingly conducted. The results are shown in Table 4 (1) and (2). In areas with higher income level, pilot guided activities have a
positive and signicant impact on residents’ green lifestyle, while in areas with lower income level, pilot guided activities have no
signicant impact. The possible reason is that green lifestyle has higher moral attribute than functional attribute, and residents with
low income may do not care about its moral attribute [44]. For high-income residents, they are more able to buy energy-saving
products [45,46] and are willing to actively pursue the moral attributes brought by a green lifestyle under the inuence of policies.
4.4.2. Heterogeneity analysis of educational level
People with higher levels of education tend to be more environmentally friendly [47]. The higher the education level, the more
likely they are to classify garbage, choose eco-label food and save water [48,49]. People with higher education are may have more
environmental knowledge, and this is believed to have a causal relationship with environmental behavior. Therefore, in order to test
the heterogeneity of residents’ education level, the whole samples are divided into high and low groups and difference-difference tests
are accordingly conducted.
The results are shown in Table 4 (3) and (4). For residents with high education level, the inuence of pilot guidance activities on
green lifestyle is positive and signicant. Whereas, for the group with low education level, the coefcient fails to pass the signicance
test. This indicates that for well-educated people, the national low-carbon action plan will lead them to change their green lifestyle,
and this guiding effect does not exist for residents with low education level. A reasonable explanation is that highly educated residents
can understand complex environmental issues well, therefore, they become more concerned about environmental quality and are more
willing to engage in green consumption behaviors [50,51]. In addition, they also have the ability to transform their extensive lifestyle
into a green and low-carbon one.
5. Additional analyses
5.1. Environmental awareness
According to Albrecht et al.(2010) [52], residents have different levels of attention to the environment and may have different
levels of acceptance of green lifestyle under the requirements of pilot guidance activities. Therefore, in order to test the regulatory
effect, we introduce the centralized environmental awareness and its interaction with pilot guidance activities, and establish the
environmental awareness regulation model as follows:
Green lifestyleit =
α
0+
α
1Activityit +
α
2Cawareit +
α
3Activityit*Cawareit +
α
∑Zit +
μ
i+
ν
t+
ε
(4)
Where C_aware
it
represents the post-centralized environmental awareness, Activity
it
*C_aware
it
represents the cross term between pilot
activities and post-centralized environmental awareness.
The results are shown in Table 5 (1). The interaction term coefcient is signicantly positive, which reects that environmental
awareness has a positive regulating effect on guidance activity pilot and residents’ green lifestyle. In building a green lifestyle, there
are no bystanders. A good ecological environment is a necessary condition for a better life [53]. Residents with high environmental
awareness are more aware of their environmental conditions and have a higher initiative to improve the current poor ecological
environment. As a result, it can take actions more quickly to respond [54].
Table 5
Mechanism analyses.
(1) (2
Activity 0.091** 0.065***
(0.043) (0.023)
c_aware 0.035 
(0.045) 
Policy* c_aware 0.369* 
(0.19) 
c_Tech 0.821
(0.753)
Policy* c_Tech 9.887***
(1.642)
Control variable Yes Yes
Year xed effects Yes Yes
Provincial xed effect Yes Yes
_cons −1.143 1.075
(1.527) (1.169)
Observations 288 264
R-squared 0.747 0.858
***p <.01, **p <.05, *p <.1. Standard errors are in parentheses.
Z. Wei et al.
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9

5.2. Green innovation
The realization of green lifestyle requires systemic changes in both supply and demand side, and need the support of more green
innovation practices in the eld of life [55]. For instance, when the public chooses greener new energy vehicles as travel tools, it needs
convenient charging technology and other supporting systems. In order to test the regulating effect of it, we introduce the centralized
technology innovation variable and its interaction term with pilot guidance activities, and establish the regulation model of green
technology innovation level as follows:
Green lifestyleit =
α
0ʹ+
α
1ʹActivityit +
α
2ʹCTechit +
α
3ʹActivityit*CTechit +
α
∑Zit +
μ
i+
ν
t+
ε
(5)
Where C_Tech
it
represents the regional green innovation level after centralization, Activity
it
*C_Tech
it
represents the cross term be-
tween pilot activities and regional green innovation level after centralization, and other variables are consistent with the benchmark
regression.
The test results are shown in Table 5 (2). The interaction coefcient is positive and statistically signicantly, suggesting that the
green innovation has a positive impat on pilot guidance activities and residents’ green lifestyle. The possible explanation is that the
green innovation level represents the regional green production level to some extent, and the green production level is closely related
to the living standard [56]. When the regional green innovation level is at a high level, the production cost of green products can be
reduced, thus eliminating the high consumption threshold brought by the expensive premium and making the cost of choosing green
lifestyle lower for residents, this makes people more willing to make a green living transition.
6. Conclusion
6.1. Conclusion and discussion
This study utilizes panel data encompassing 24 provinces in China form 2008 and 2019 to empirically examine the inuence of
China’s initial large-scale national low-carbon pilot program on residents’ direct response to climate change, specically their
adoption of green lifestyles. Employing a multi-period DID method, the study arrives at several key ndings.
Firstly, the empirical results demonstrate a signicant enhancement in residents’ green lifestyles due to the implementation of the
activity. This conclusion remains valid even after conducting a range of robustness tests. This implies that the external policy envi-
ronment signicantly inuences changes in the individual lives of residents. On one hand, residents exhibit sufcient sensitivity to
environmental protection [57]. When the nationwide low-carbon initiative fosters a societal atmosphere of environmental conser-
vation, residents’ lifestyles gradually transition towards becoming more environmentally friendly, aligning with existing policy re-
quirements. On the other hand, as a non-coercive policy, the nationwide low-carbon initiative, compared to coercive policies,
possesses greater exibility. Residents are more receptive, allowing for a more effective implementation of the policy [58], supporting
the conclusions of this study regarding the promotion of green electricity usage among residents through non-coercive policies.
Secondly, heterogeneity analyses show that the impact of pilot activities in regions with higher income levels surpasses that in regions
with lower income levels. Correspondingly, the inuence of guidance activities in areas with greater educational attainment is higher
compared to areas with lower educational levels. As investigated by Wu et al. (2021), higher regional economic levels correspond to
increased guarantees of urban green facilities, exemplied by elevated levels of green spaces [59]. This forms a robust foundation for
the residents’ green lifestyle transformation [60]. Regions with higher educational attainment tend to prioritize residents’ happiness.
Krekel et al. (2016) observed that the greener the city, the greater the residents’ happiness [61]. This afrms the conclusion of this
study that higher education levels are associated with a greater willingness to undergo a green lifestyle transformation.
Lastly, this study identies that residents’ environmental awareness and the green innovation signicantly have a positively
moderate impact. These two factors are crucial in enhancing the positive impacts of the pilot program. Enhancing residents’ envi-
ronmental awareness is a crucial pathway through which the national low-carbon pilot inuences their lives. According to Liu and Xu
(2022), low-carbon city pilots have succeeded in elevating residents’ environmental awareness, leading to a reduction in carbon
emissions [17]. This illustrates that the level of regional innovation serves as a cornerstone for supporting residents’ green living. Li
et al. (2019) posited that innovation drives industrial development [62]. As innovation fosters the emergence of green industries,
residents incorporate more environmentally friendly choices into their lives, inevitably increasing the overall sustainability of their
lifestyles.
In conclusion, this study systematically presents compelling evidence of the positive impact of China’s national low-carbon pilot
program on residents’ green lifestyles, highlighting the crucial role of individual and regional characteristics in achieving program
effectiveness. From a theoretical standpoint, this article delves deeply into the transformation process of residents’ lifestyles from a
distinctive policy pilot perspective. This not only broadens the research scope concerning green transformation at the residents’ level
but also furnishes crucial theoretical underpinnings for the established research framework on policy-driven green transformation in
residents’ lives. In terms of practical application, the study not only validates the policy’s efcacy and offers a signicant reference for
the region to achieve profound decarbonization but also contributes valuable insights for establishing a global-scale policy system
promoting green, low-carbon, and circular development. We aspire that this research will enlighten the international community on
sustainable development and low-carbon transition, providing empirical support for a globally coordinated response to the challenges
of climate change, thereby signicantly contributing to the realization of global sustainable development goals.
Z. Wei et al.
Heliyon 10 (2024) e38665
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6.2. Policy implication
The above ndings have signicant managerial implications. Firstly, it is crucial to expand the impact of pilot low-carbon activities
and strategically enhance their effectiveness. The pilot program’s success relies on comprehensive, coordinated, and sustainable
development. Therefore, it is essential to foster harmonious interactions between energy, environment, and the enhancement of in-
dividuals’ quality of life. This can be achieved through continual improvements in supporting policies and measures, expediting the
transition toward a new era of green production and lifestyles. It is important to actively explore systematic approaches and mech-
anisms that facilitate residents’ adoption of green lifestyles. Additionally, implementing relevant policy incentives and mechanisms
can further reinforce the long-term effects of pilot guidance activities. Overall, by strategically expanding the impact of pilot low-
carbon activities and fostering a supportive framework, we can accelerate the shift towards a sustainable future.
Secondly, we should rationally formulate pilot guidance activities in different regions, exert different degrees of pressure on
provinces at different stages of development, and make the implementation of guidance activities more exible and inclusive. In
provinces with higher income levels and higher education levels, we should continue to optimize the green living standards of resi-
dents, and enhance the radiative driving role of economically developed regions to speed up economic development and environ-
mental protection in other regions. For provinces with low-income levels and low education levels, we need continue to promote the
pilot in accordance with the principle of gradual progress, and gradually promote the transformation of residents’ lifestyles into
greener ones.
Thirdly, the government needs to raise the awareness of green consumption of urban residents and give full play to their initiative.
On the one hand, through themed activities, school education, supervision and restraint, we will strengthen the awareness of green
development and environmental awareness of the whole society, and encourage residents to gradually form green consumption habits.
On the other hand, the democratic and open nature of the government’s environmental governance should be improved. The open and
transparent participation process can exercise and improve stakeholders’ awareness of environmental issues, thus indirectly gener-
ating pressure from outside the process and requiring decision-makers to make decisions of high environmental quality.
Fourthly, it is imperative to prioritize the strengthening of green innovation and the establishment of green supply chains. The
adoption of a green lifestyle necessitates scientic and technological advancements, as it cannot be dissociated from the overall
improvement of the society’s green technology level. Green technology innovation plays a pivotal role in developing new products,
services, and solutions that cater to the increasing demand for sustainable living. According to the socio-technological systems theory
proposed by Geels (2004), when social and technological systems are effectively integrated, they generate better overall outcomes.
Therefore, the pursuit of a sustainable development path must consider not only green technology or green lifestyle in isolation, but
also the establishment of an effective linkage mechanism among key social actors such as enterprises, governments, universities,
research institutes, and technology users. By effectively combining technological innovation with social aspects of life, we can
accelerate industrial transformation and upgrade various sectors. This collaborative effort will result in collective progress in economic
development, social governance, and ecological advancement. By fostering regional green innovation and building robust green supply
chains, we can lay the foundation for a sustainable future, one that harmonizes economic growth, social development, and ecological
preservation.
6.3. Limitations and future research
Firstly, despite measuring residents’ green lifestyle across three dimensions, the complexity of green lifestyle practices remains
multifaceted. The employed measurement method in this article may not fully encapsulate its entirety. Subsequent research should
delve into more comprehensive approaches to gauge the extent of greening in residents’ lives.
Secondly, while this study focuses on the comprehensive low-carbon pilot policy, offering insights into the impact of external
policies on residents’ lifestyles to a certain extent, there are still other potential policies that remain unexplored. In practice, gov-
ernment policy systems exhibit considerable diversity. Subsequent research should consider integrating various policies for a more
systematic investigation, enhancing the effectiveness of policy interventions.
Finally, the scope of this study investigation is limited to the provincial level. Employing more nuanced research samples could
open up additional research avenues. For instance, exploring the impact of policies at a micro level, considering factors such as family
living conditions and personal needs, could unveil horizontal inuence mechanisms.
Funding
This work was supported by the National Natural Science Foundation of China (72204243); Ministry of Education, Humanities and
social science research projects (20YJC630138); Special Fund of University of Science and Technology of China (YD2160004005;
YD2040002016).
Ethic statement
This is an observational study, and we conrmed that no ethical approval is required.
Z. Wei et al.
Heliyon 10 (2024) e38665
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Data availability statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Consent to participate
Not applicable.
Consent to publish
Not applicable.
CRediT authorship contribution statement
Zhengyun Wei: Writing – original draft, Methodology. Liang Wan: Writing – review & editing, Funding acquisition, Conceptu-
alization. Qiaoqiao Zheng: Formal analysis, Data curation. Zexian Chen: Writing – original draft, Formal analysis. Shanyong Wang:
Supervision, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
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