Green Consumption, Environmental Regulation and Carbon Emissions—An Empirical Study Based on a PVAR Model

Abstract

China’s proposed double carbon goal makes it urgent to promote green consumption and green lifestyles. The present study selected separate economic indicators of urban and rural areas and assigned different weights in the construction of a green consumption indicator system. Based on data from 30 provinces (excluding Tibet) between 2003 and 2019, this study investigated the connections between green consumption, environmental regulations, and carbon emissions, analyzing their mechanism. This study found that green consumption will reduce anthropogenic carbon emissions in the short term but will result in low carbon emissions in the long term. Environmental rules have a definite long-term impact on green consumption, as evidenced by the “U”-shaped trend they follow. Second, this study found that the level of green consumption exhibits a rising and then falling trend on the vegetation’s capacity to sequester carbon, and the impeding force will become stronger over time. Third, this study found that green consumption innately has a degree of inertia and self-enhancement bias.

1. Introduction

Improvements in people’s living standards have coincided with the large-scale phenomenon of irrational and excessive consumption, which has led to considerable resource waste and environmental pollution. Unreasonable consumption modes have had a significant negative impact on resources and the environment, and the healthy development of economies requires a green transformation of consumption. In September 2020, China stated that it would “strive to peak its carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060”. In recent years, consumption has accounted for approximately 40% of China’s total gross domestic product (GDP), and China’s proposed double carbon goal makes it urgent to promote green consumption and green lifestyles.

The two major strategies vis-à-vis attaining carbon neutrality are to decrease carbon emissions, or the carbon emissions brought on by human activity, and to enhance carbon sinks, or the ability of carbon dioxide to be absorbed by various mechanisms. In terms of climate change, the two are complementary, and in line with policies aimed at reducing carbon emissions, fully supporting the growth of carbon sink capacity is a crucial step toward becoming carbon neutral.

The rise of social productivity is what gives rise to green consumerism. Natural resource depletion and ecological environment devastation are the results of the sharp rise in human-consuming activities that have accompanied the creation of material affluence. On the one hand, environmental issues could be brought on by the private household sector [1]; On the other hand, regulatory subsidies for corporate environmental behavior, the necessity for green products, public awareness of environmental protection, and associated consequences for corporate vandalism can also compel firms to prioritize environmental issues [2], all of these approaches have a positive effect on the achievement of the dual-carbon goal. Nevertheless, China’s current green consumer development has several drawbacks. First, raising people’s awareness of green consumption still takes time. It is a long and gradual process that will require the support of the majority of consumers who have a high level of ecological, environmental, and responsibility awareness as well as other humanistic qualities to realize a higher level of green consumption. Second, there is a deficiency in the availability of green consumer goods and services. The lack of key core technology mastery, low enterprise motivation for green product production, weak profitability of green products, and numerous other issues limit the availability of green products in China. Third, there is a need to enhance the green consumer protection framework. China’s system for gaining market access to green products is still far from ideal, and consumers’ legitimate rights and interests in protecting their green consumption are still developing.

To investigate what kinds of interactions among the three are required to speed up the process of green consumption, we have chosen to start this paper by examining the current state of green consumption and the interaction between the degree of environmental regulation and the level of carbon emissions, which is less studied at the moment. How much time must pass before green consumption can benefit from the effects of environmental regulating policies? Will a greater level of green consumption be realized as a result of increased public awareness of green consumption brought about by improvements in carbon emission efficiency? The following issues also need to be covered in this paper: how can the amount of green consumption be assessed scientifically? Which strategies work best to encourage the growth of environmentally friendly consumption? To give a theoretical and empirical foundation for the growth of green consumption in China, this study will concentrate on the aforementioned major challenges.

This paper is organized as follows: The first section primarily begins with the current state of China’s green consumption, the level of environmental regulation, and the status quo of carbon emissions. It then analyzes the innovations and shortcomings of the current research on green consumption and identifies the issues that the article seeks to address further. The second section covers the paper’s investigation method and related research theories. The third section introduces the index selection. The fourth section deals with the choice of measurement methods and empirical findings. The fifth section provides the paper’s conclusions and recommendations.

2. Literature Review

From the perspective of sustainable consumption, there are two main evaluation indicators: one is the sustainable consumption indicator framework established by the OECD, and the other is the core indicator system of consumption and production mode change proposed by the UNCSD. However, these two index systems differ from the actual situation in China; for example, the UNCSD only targets green commodities themselves and is less involved in the production and life of residents, so we need to build evaluation indicators that are in line with China’s reality. Green consumption stems from sustainable development, which comprises three stages: the purchase, use, and disposal of products [3]. As a new consumption concept, it faces a series of new challenges and problems in practical exploration and theoretical research. Past studies have been conducted from five perspectives: green consumption theory [4,5,6,7], a green consumption model [8,9,10], green consumption marketing strategy [11,12,13], the green consumption attitude–behavior gap [14,15,16], and factors affecting green consumption [17,18,19,20,21].

Research on green consumption from different perspectives has mainly focused on the individual level, analyzing and studying the impact of demographic variables such as gender, age, education, awareness concepts, and income on individual green consumption behaviors through sample surveys. Current research on green consumption is mainly based on a macro perspective [22,23,24]. The construction of the green consumption indicator system has the following features: most of the studies that have already been done choose indicators based on the availability of indicators in the study region and the researchers’ own subjective definition of green consumption; there is no clear trend in the selection of green consumption indicators the majority of indicators that can accurately represent green consumption are hard to obtain. In terms of study scope, the majority of the literature now in publication concentrates on annual comparison studies, with comparatively little attention paid to smaller-scale regional studies, and the studies’ conclusions are not intended for practical use. The aforementioned explanations explain why the current research on green consumption is constrained in scope and exhibits a certain lack of objectivity in the indicators chosen. This doesn’t stop us, though, from using the knowledge gained from earlier studies to conduct a more in-depth analysis of green intake. This work broadens the research scope to develop China’s provincial green consumption index to address the aforementioned issues, analyze China’s green consumption from a more in-depth angle, and improve the study’s applicability. We have selected shared bicycles and new energy cars as our indicators since they are novel, and popular, and haven’t been covered in any previous research. For the construction of provincial Chinese green consumption index (GCI) systems, the existing literature has mainly focused on three approaches, namely, mixing urban and rural economic indicators [20,21,25,26], constructing urban GCIs using urban economic indicators [27], and using economic indicators to construct rural GCIs [28].

There has been minimal research on green consumption in production and living, with the majority of current studies on environmental regulation and consumption concentrating on energy consumption in the industrial production sector. Research has shown that rising energy consumption raises carbon emissions before the implementation of environmental regulations and that this effect quickly diminishes as environmental regulations are tightened [29]. This research investigates if green consumption, which is significant and relevant to our lives, will be regulated by the environment similarly to energy use. The majority of research has concentrated on carbon emissions while paying insufficient attention to carbon sink capabilities. To fully understand the factors affecting carbon neutrality, we must examine a system’s capacity for carbon sinks as well as its carbon outputs.

In this study, we first constructed a GCI based on the results of previous studies; we selected indicators for urban and rural areas and assigned them different weights so that the constructed indicators could more accurately reflect the current situation of green consumption in China. Second, environmental regulations (ERs) and carbon emissions were introduced to analyze the factors influencing green consumption. Third, carbon emissions were not measured by a single indicator but appraised in terms of anthropogenic carbon emissions (ACE) and the carbon sequestration capacity of vegetation (CSCV).

3. Construction of Green Consumption Indicator System

According to sociological definitions, human consumption behaviors can be broadly classified into two categories, namely, productive and subsistence consumption, with the former referring to expanding reproduction and the latter referring to human needs. Both types of consumption behaviors can implement the green consumption concepts of thrift, reasonableness, health, and civilization.

Regarding the construction of provincial Chinese GCI systems, the existing literature mostly mixes urban and rural economic indicators [20,21,25,26], but China’s unique urban–rural dual economic system and the large differences in production and lifestyles between urban and rural areas may lead to weight mismatch and affect the reasonableness of the indicators when the urban and rural areas are mixed to construct GCIs. This may lead to weight mismatch and affect the rationality of the indicators. In addition, China’s arable land area accounts for approximately 13.3% of the country, which is enough to show that the rural economy is a crucial part of China’s national economy that cannot be ignored when studying green consumption. Therefore, this study separately selected economic indicators of urban and rural areas and assigned them different weights so that the constructed indicators could more accurately reflect the current situation of green consumption in China.

3.1. Urban Green Consumption Indicator

Urban green consumption includes the greening of not only production processes but also living and traveling consumption processes. Urban green production is mainly embodied in the industrial technology efficiency and rationalization of resource utilization, specifically in the industrial production process, by strengthening the use of raw materials; controlling waste gas, wastewater, and solid waste emissions; and applying other treatment methods that can effectively reduce environmental pollution. Since China has a unique resource endowment and a distinct way of life for its people, there are no rigorous standards when it comes to the selection of urban green indicators. As a result, the majority of the indicators included in this study are from domestic research that has already been done. For example, the proportion of clean energy consumption, Urban heating area, and air quality [19,21,30]. Whether or not it is a green home lifestyle or a green way of traveling, all reflect the connotation of green consumption, which improves the quality of residents’ green lifestyles. As we previously stated, there is no rigid standard for choosing urban green indicators because of China’s distinct resource endowment, people’s way of life, and production style. As a result, the majority of the indicators included in this study are from domestic research that has already been conducted. The gradual popularization of new energy vehicles has reduced pressure on the transportation industry, which has a high degree of environmental pollution, and enhanced the green lives of cities (

Table 1. Urban green consumption indicator.

Urban	Indicator	Unit
	CO2 emissions per unit of GDP	10,000 tons/GDP
	Proportion of clean energy consumption	%
	Wholesale, retail, and food service industries’ carbon intensity	%
	Carbon emission intensity of transportation, warehousing, postal, and telecommunications services sector	%
	Urban heating area	10,000 square meters
	Rate of harmless treatment of municipal domestic waste	%
	Unit GDP living consumption sector gasoline consumption	10,000 tons standard coal/GDP
	Total number of products effectively using green food labels	number of labels
	Number of visits to A-level tourist attractions	number of persons
	Sales of new energy vehicles	number of automobiles
	The average number of days when the air quality in provincial capitals and municipalities reaches or is better than level 2	quantity of days
	Public transportation vehicles per 10,000 inhabitants in cities	tens of thousands of persons/vehicle
	Urban green park area per capita	square meter

).

3.2. Rural Green Consumption Indicator

Similar to urban areas, the selection of green consumption indicators in rural areas is predicated on current research. Compared with green consumption in cities, that in rural areas mainly focus on agricultural production. China’s green agriculture practices adhere to the concept of strengthening the prevention and control of agricultural pollution at source and strengthening the protection and restoration of the agricultural ecosystem, so we selected indicators such as chemical fertilizer and mulch film for evaluation. In addition, coal and water heaters are indispensable to the lives of rural residents, so they were also included in the accounting indicators (

Table 2. Rural green consumption indicators.

Rural	Indicator	Unit
	Amount of fertilizer used for agriculture per irrigated acre of land	10,000 tons/1000 hectare
	Area covered by agricultural mulch	hectare
	Water-saving irrigation area	hectare
	Unit GDP living consumption sector coal consumption	10,000 tons standard coal/GDP
	Cultural stations in rural communes	number of stations
	Rural solar water heater area	10,000 square m2
	Electricity consumption in rural areas per capita	10,000 kWh/person

).

3.3. Measurement of Green Consumption Indicators

Based on accessibility, viability, and completeness, we selected data from 30 provinces, excluding Tibet, from 2003 to 2019. The data for each GCI in this study were obtained from the China Statistical Yearbook, China Urban Construction Statistical Yearbook, China Rural Statistical Yearbook, China Energy Statistical Yearbook, Green Food Statistical Yearbook, and Energy Saving and New Energy Vehicle Yearbook.

Because of the differences in the magnitudes of the raw data, the data were made dimensionless before the weights were determined. First, the selected indicators were divided into two categories for processing: positive and negative indicators. This ensured that negative indicators had the same direction of action as positive indicators regarding the final GCIs. Second, the indicators were processed in a dimensionless manner. Because of the selection of different indicators, the extreme data difference was large, and some indicators were heavily weighted, which affected the results of the measurements. We refer to this process as the improved efficacy coefficient method of data for dimensionless processing [31].

$$\mathrm{positive} \mathrm{indicators}: X_{ij}=40+60\times \frac{x_{ij }-x_{ijmin}}{x_{ijmax}-{ x}_{ijmin}}$$

(1)

$$\mathrm{negative} \mathrm{indicators}: X_{ij}=40+60\times \frac{x_{ijmax }-x_{ij}}{x_{ijmax}-{ x}_{ijmin}}$$

(2)

$X_{ij}$ is a dimensionless value, $i$ denotes economic indicators, $j$ indicates the region represented by each economic indicator, $x_{ij }$ represents the true value of the economic indicators, $x_{ijmin}$ is the minimum of the true value of each economic indicator, and $x_{ijmax}$ is the maximum of the true value of each economic indicator.

There are many kinds of assignment methods, including expert scoring, hierarchical analysis, fuzzy analysis, entropy value, factor analysis, and principal component analysis methods. Because some assignment methods are based on the subjective scoring of the importance of indicators by human factors, they lack a degree of objectivity and may lead to unreasonable calculation results. Therefore, we chose the entropy value method based on our actual sample data; the greater the degree of change in the data, the greater its impact on the indicators. The details are presented as follows:

(1)

Calculate the weight of sample $i$ in index $j$

$$P_{ij}=\frac{X_{ij}}{\sum _{i=1}^n{X}_{ij}}$$

(3)

(2)

calculate the entropy value $e_j$

$$e_j=-k\sum _{i=1}^n\left(P_{ij}ln\right(P_{ij}\left)\right)$$

(4)

$k=1/\ln \left(n\right)$, $n$ is the number of samples.

(3)

Calculate the difference coefficient of index $j$

The disparity between an indicator’s entropy value $e_j$ and 1 determines its information utility value, and the value directly affects the weights’ size. The greater the value of information utility, the greater the importance of the evaluation, and the greater the weight. The utility index is defined as $d_j$.

$$d_j=1-e_j$$

(5)

(4)

Calculate the evaluation indicator’s weight

$$W_j=\frac{d_j}{\sum _{i=1}^n{d}_j}$$

(6)

Through the weight determination method introduced in the previous section, the specific weight of each GCI could be calculated. Of these, the weights of urban and rural areas were expressed as their respective ratios to total GDP, and

Table 3. The weights of green consumption indicators.

	Indicators
Urban (0.7725)	CO2 emissions in relation to GDP (0.0138)
	The proportion of clean energy consumption (0.1505)
	Wholesale, retail, and food service industries’ carbon intensity (0.0506)
	Carbon emission intensity of transportation, warehousing, postal, and telecommunications services sector (0.0389)
	Urban heating area (0.0477)
	Rate of harmless treatment of municipal domestic waste (0.1341)
	Unit GDP living consumption sector gasoline consumption (0.0718)
	Total number of products effectively using green food labels (0.1318)
	Number of visits to A-level tourist attractions (0.0504)
	Sales of new energy vehicles (0.0516)
	The average number of days when the air quality in provincial capitals and municipalities reaches or is better than level 2 (0.0619)
	Per 10,000 people in cities, public transportation vehicles (0.0930)
Rural (0.2275)	Urban green park area per capita (0.1039)
	Amount of agricultural fertilizer used per unit irrigated area of cultivated land (0.0540)
	Area covered by agricultural mulch (0.0350)
	Water-saving irrigation area (0.2259)
	Unit GDP living consumption sector coal consumption (0.0317)
	Cultural stations in rural communes (0.2785)
	Rural solar water heater area (0.3397)
	Electricity consumption in rural areas per capita (0.0351)

displays the precise weight of each indication.

GCIs were measured through our constructed GCI system. We substituted the specific values of the indicators, entropy-weighted each indicator (see Equation (7) for the entropy-weighting matrix), calculated the evaluation values of the indicators of each province in the past, and finally summed these values to obtain the total GCI value of each province in the years from 2003 to 2019.

$$\mathrm{y}=\left[\begin{array}{cccc}\hfill {\mathrm{w}}_1{\mathrm{x}}_{11}& {\mathrm{w}}_1{\mathrm{x}}_{12}\hfill & \hfill \dots & {\mathrm{w}}_1{\mathrm{x}}_{1\mathrm{n}}\\ \hfill {\mathrm{w}}_2{\mathrm{x}}_{21}& {\mathrm{w}}_2{\mathrm{x}}_{22}\hfill & \hfill \dots & {\mathrm{w}}_2{\mathrm{x}}_{2\mathrm{n}}\\ \hfill ⋮& ⋮\hfill & \hfill ⋮& ⋮\\ \hfill {\mathrm{w}}_{\mathrm{m}}{\mathrm{x}}_{\mathrm{m}1}& {\mathrm{w}}_{\mathrm{m}}{\mathrm{x}}_{\mathrm{m}2}\hfill & \dots & {\mathrm{w}}_{\mathrm{m}}{\mathrm{x}}_{\mathrm{m}\mathrm{n}}\end{array}\right]$$

(7)

4. Empirical Test and Results Analysis

4.1. Method

A traditional panel regression model presets endogenous and exogenous variables before conducting analysis, assuming that there is a theoretical relationship between the economic variables, which, to a certain extent, cannot avoid the endogeneity of variables due to subjective judgment. A panel vector autoregressive (PVAR) model provides a good solution to this problem. In a PVAR model, it is assumed that all variables are endogenous without considering whether there is a causative connection between several factors and only considering the change that occurs when a variable is shocked with its lagged term and other variables. Such a model also has the advantages of controlling individual heterogeneity and eliminating temporal variability after differentiation. The model is shown in Equation (8).

$$Y_{it}={\alpha }_0+{\sum }_{j=1}^p{\alpha }_j{Y}_{i,t-j}+v_i+{\mu }_i+{\epsilon }_{it}$$

(8)

$Y_{ij}$ represents the core variables of GCI, ER, ACE, and CSCV; $i$ denotes the sample; $t$ denotes the year; ${\alpha }_0$ is the intercept term; ${\alpha }_j$ is the matrix of model coefficients; $p$ is the lag period; $v_i$ denotes the sample individual effect; ${\mu }_i$ denotes the sample time effect; and ${\epsilon }_{it}$ is the random error term.

4.1.1. Environmental Regulation (ER)

There are four main metrics for ER: the single-indicator, composite index, categorical examination, and assignment score methods. Because the single-indicator method only measures one aspect of ER, such as the environmental governance input funds that most previous studies have chosen, it can easily result in bias in biased conclusions. Furthermore, the categorical examination and assignment score methods are somewhat subjective. Owing to the limitation of data availability, we finally chose the composite index method to measure ER. Drawing on the measurement method of Li and Zou [32], five single indicators were selected: industrial smoke (dust) emissions, industrial SO₂ emissions, the comprehensive industrial solid waste utilization rate, the harmless treatment rate of domestic waste, and the centralized treatment rate of wastewater treatment plants. The method of measuring the ER index was consistent with the method of measuring GCI values in the previous section.

4.1.2. Anthropogenic Carbon Emissions (ACE)

Chen [33] believes that the target of carbon neutrality is anthropogenic rather than natural and that achieving carbon neutrality requires a balance between ACE and anthropogenic carbon removal. Currently, some scholars have concluded that the carbon emissions from social production and living in China are relatively high, and they indirectly indicated that the root cause of a large amount of carbon emissions originates from human beings. In this paper, carbon emissions from human activities were measured at the county level in China based on data provided by the Carbon Accounting Database (CEADs). Here, the county-level data were aggregated at the provincial level to obtain the ACE of each province.

4.1.3. Carbon Sequestration Capacity of Vegetation (CSCV)

There are two main methods for anthropogenic carbon removal: industrial carbon sink technology and vegetation carbon sinks. At present, China’s industrial carbon sink technology is still in the preliminary stages, and there is still a certain way to go from large-scale production has yet to be reached. Therefore, natural carbon sinks have become the main subjects of carbon sink capacity measurements. Because it is difficult to accurately estimate the carbon sequestration capacities of oceans, lakes, and organisms, this study only considered the carbon sink capacity of terrestrial vegetation. Vegetation carbon sequestration data were also measured concerning the CEADs, and the net primary productivity (NPP) of China’s vegetation surface was obtained through the MOD17A3 project of NASA’s Earth Observing System. Notably, NPP refers to the net carbon sequestration of green plants through photosynthesis, which is mostly used to examine the carbon sequestration ability of surface vegetation in terms of the remaining portion of the total organic matter produced by photosynthesis per unit time per unit area after deducting autotrophic respiration. Chen et al. [34] matched NPP data to the county level with photosynthesis carbon conversion coefficients according to different vegetation types (green coniferous forest, evergreen broad-leaved forest, deciduous broad-leaved forest, deciduous broad-leaved forest, mixed forest, closed scrubland, open shrubland, xerophytic savanna, savannah, grassland, and farmland), and in this paper, the data were aggregated to the provincial level to obtain CSCV values.

To become carbon neutral, it is important to consider both carbon emissions and the capacity of vegetation to absorb carbon. The capacity of terrestrial ecosystems to sequester carbon has recently varied significantly across space due to climate change and human activity. Because only one ACE scenario can be used to accurately measure each province’s carbon emission levels, an ecosystem’s ability to absorb carbon is of particular significance. As a result, this study based its measurement of ultimate carbon emissions on two indicators: anthropogenic carbon emissions and the ability of the vegetation to sequester carbon.

4.2. Stability Test

To avoid the phenomenon of pseudo-regression among the data, we used five-unit root tests, namely, the LLC, IPS, Fisher ADF, and Fisher PP tests, to verify whether the data were smooth. According to

Table 4. Descriptive statistics of variables.

Variables	Sample Size	Mean	Standard Deviation	Minimum	Maximum
GCI	510	26.077	1.991	21.465	31.017
ER	510	57.170	10.201	40.843	85.739
ACE	510	553.467	392.855	31.167	1874.234
CSCV	510	4285.508	2953.76	220.256	13,009.84

, the GCI, ER, and CSCV passed the five-unit root tests while the Fisher PP of ACE did not. Collectively, they were more stable than unstable; therefore, these data are considered to be stable. The selected variables in the study did not need to be differenced, and there was a cointegration relationship; thus, the PVAR model could be built (

Table 5. Unit root test results of each variable.

Variable	LLC	IPS	Fisher ADF	Fisher PP
GCI	−4.0030 ***	−2.7704 ***	−1.4119 *	−4.0247 ***
ER	−7.8072 ***	−2.3417 ***	−2.5165 ***	−1.4803 **
ACE	−7.2582 ***	−3.8319 ***	−2.0702 **	−0.0747
CSCV	−7.4879 ***	−2.2029 ***	−7.0177 ***	−11.7301 ***

***, **, and * mean that the t-value is significant at the levels of 1%, 5%, and 10%, respectively.

).

4.3. Determination of Lag Period

It was essential to determine the ideal lag period for the PVAR model before conducting an estimation, if the lag period is too large, it will cause excessive loss of degrees of freedom and affect the validity of model estimation; If the lag is too small, too much sample data will be lost. In following the standard of reducing the model’s AIC, BIC, and HQIC values, the optimal lag period of the PVAR model for the GCI, ER, and ACE could be determined as the first period, while the optimal lag period of the PVAR model for the GCI, ER, and CSCV was determined as the fourth period. The outcomes are displayed in

Table 6. Lag period test results.

GCI, ER, and ACE				GCI, ER, and CSCV
Lag Period	AIC	BIC	HQIC	Lag Period	AIC	BIC	HQIC
1	16.3425 *	17.2465 *	16.6988 *	1	20.6411	21.5452	20.9974
2	16.8187	17.8576	17.2294	2	19.7558	20.7947	20.1664
3	16.8814	18.0713	17.3531	3	19.4631	20.6529 *	19.9348
4	17.5774	18.9376	18.1182	4	19.3816 *	20.7417	19.9224 *

* mean that the t-value is significant at the levels of 1%, 5%, and 10%, respectively.

.

4.4. GMM Estimation of the PVAR Model

After determining the optimal lag order, the GCI and ER were analyzed by constructing PVAR models with ACE and the CSCV, respectively, for a generalized method of moments (GMM) estimation. The conclusions are displayed in

Table 7. GMM estimation results of the PVAR models.

GCI, ER, and ACE				GCI, ER, and CSCV
Variable	h_GCI	h_ER	h_ACE	Variable	h_GCI	h_ER	h_CSCV
L.1_GCI	11.62 ***	3.81 ***	−3.46 **	L.1_GCI	4.03 ***	1.91 *	−1.74 **
L.1_ER	−3.29 ***	13.51 ***	−2.43	L.1_ER	−2.10 **	11.59 ***	−0.74
L.1_ACE	−1.81 ***	1.49 **	8.26 ***	L.1_CSCV	−0.31	0.56	2.73 ***
				L.2_GCI	−0.76	1.15	3.38 ***
				L.2_ER	0.01	0.61	−1.44
				L.2_CSCV	−0.86	1.38	1.90 *
				L.3_GCI	1.75 *	−2.13	−2.86 ***
				L.3_ER	−0.46	1.43 **	2.11 **
				L.3_CSCV	0.14	1.30	−1.18
				L.4_GCI	−0.89	0.13	0.84
				L.4_ER	−0.85	−0.30	−2.45 **
				L.4_CSCV	0.30	0.13	3.36 ***

***, **, and * mean that the t-value is significant at the levels of 1%, 5%, and 10%, respectively.

.

In the estimation of the GCI, ER, and ACE, the GCI with a one-period lag had a substantial beneficial effect on carbon emissions, indicating that the green lifestyle and production modes of residents have obvious driving effects on reducing carbon emissions. Moreover, ER with a lag was found to have a significant negative impact on the GCI, which did not cause a rapid increase in residents’ green consumption ability in the short term because it took some time for ER policies to take effect. The lagged carbon emissions were found to have a substantial negative impact on the GCI, which indicates that carbon emission levels affect residents’ choice of a green lifestyle. The lagged carbon emissions had a beneficial effect on ER, indicating that the higher the carbon emission level, the stronger the ER.

In the model estimation of the GCI, ER, and CSCV, the lag period of ER had significantly different effects on the CSCV, and ER with lag one, and lag two had negative impacts on the CSCV, indicating that ER in the short term cannot improve carbon sequestration capacities. When lagging the three periods, the ER coefficient turned from negative to positive, and the result became significant. This indicates that ER policies, after a while, play a strong positive role in maintaining the CSCV. However, this does not mean that ER always has a positive effect. When the variable was lagged for four periods, the ER coefficient turned from positive to negative, which indicates that excessive regulation does not enhance the CSCV but rather has a negative effect.

4.5. Impulse Response Function Analysis

Impulse response functions are used to illustrate the dynamic relationship between green consumption, environmental regulation, and carbon emissions, as GMM estimation only presents the static interplay between these three variables. The impulse response is the response of an endogenous variable to an error shock, assuming that the other factors are held constant. Specifically, this describes the impact of applying a shock of one standard deviation scale to the random error element on an endogenous variable’s present and future values. Figure 1 and Figure 2 show plots of the impulse correspondence functions of the GCI and ER with ACE and the CSCV, respectively, after Monte Carlo simulations with 200 lags and 10 periods.

The response values are represented on the vertical axis, the number of delays is represented on the horizontal axis, the middle solid line represents the impulse response function, and the solid lines on both sides represent the 95% confidence intervals for the 200 simulations. (The red line represents the trend influenced by the shock variable, and the green and blue lines represent the 95% confidence interval). Our conclusions are as follows:

• The GCI, ACE, and ER respond quickly to shocks from themselves; the GCI and ACE were significantly positive in the first five periods, indicating that the variables have strong economic inertia; and ER maintained a positive effect on shocks from themselves for a longer period, with a better self-enhancing effect;
• Regarding the impact of the GCI, ACE fluctuated greatly in the first five periods and gradually stabilized after the sixth period, showing a trend of first decreasing, and then stabilizing. This shows that green consumption had a lag and gradually affected emission reductions over time, especially after the sixth period, when carbon emissions almost approached zero, indicating that the role of green consumption in carbon emission reduction in China cannot be underestimated in the long run. When impacted by green consumption, similar to the trend of man-made carbon emissions, ERs also showed a pattern of first decreasing and then stabilizing. The reason for this trend may be that the green consumption mode has a subtle impact on people’s consumption consciousness, and people will unconsciously turn green and avoid carbon, which makes the scale of ERs smaller than before;
• Regarding the impact of ER, ACE showed a gradual upward trend while overall negative. This indicates that ERs have a significant inhibition effect on ACE. In the early stages, the effect of ER was found to be strong, and the speed of carbon emission reduction was fast. As time passed, the degree of regulation further increased and the emission reduction effect in the later stage became weaker than before. The green consumption index changed from being negative to being positive under the influence of environmental regulation throughout the first two periods, which was also consistent with the prior trend of trailing regional distribution of environmental regulation. This shows that the higher the degree of the greening of consumption, the stronger the incentive degree of ER investment. This may be because residents’ green production lifestyles have spawned some new industries, resulting in increased financial support. It was also found that ER results in a strong and long-term self-enhancement effect if the government is willing to invest more in it;
• The CSCV showed quick impact responses. In the first two stages, vegetation was disturbed by external factors and showed a strong self-recovery ability that gradually stabilized in later stages. The impact of Ers on the CSCV was first negative and then positive, which may have been due to the time lag in policy implementation. Although the impact was negative before the delay of one period, it gradually weakened until it turned positive and remained stable after the second period, indicating that the impact of Ers on the CSCV was sustained. Meanwhile, the impact of the CSCV on ER was significantly negative, indicating that the enhancement of vegetation’s carbon absorption capacity reduces the carbon pressure on society and financial investment in environmental governance. Regarding the impact of the GCI, the CSCV showed a positive impact in the first phase, indicating that the green production and living mode would be beneficial for the self-repair of the natural environment. After the second phase, the positive effect turned into a negative effect, which might have been because the green production mode of residents drove the expansion of some industries, and enterprises chose to sacrifice the natural environment for economic benefits; this is known as external diseconomy.

4.6. Granger Causality Test

To verify whether a causal relationship existed between the variables, we conducted a Granger causality test. The test results are listed in

Table 8. Granger causality test results.

	Variable		F	Results
GCI, ER, and ACE	GCI	ACE	11.955 ***	ACE is the Granger cause of GCI
		ER	5.9006 **	ER is the Granger cause of GCI
	ER	ACE	3.2912 *	ACE is the Granger cause of ER
		GCI	10.81 ***	GCI is the Granger cause of ER
	ACE	GCI	2.2173	GCI is not the Granger cause of ACE
		ER	14.519 ***	ER is the Granger cause of ACE
GCI, ER, and CSCV	GCI	CSCV	2.0338	CSCV is not the Granger cause of GCI
		ER	7.2259	ER is not the Granger cause of GCI
	ER	CSCV	2.9462	CSCV is not the Granger cause of ER
		GCI	10.405 **	GCI is the Granger cause of ER
	CSCV	GCI	15.615 ***	GCI is the Granger cause of CSCV
		ER	8.1259 *	ER is the Granger cause of CSCV

***, **, and * mean that the t-value is significant at the levels of 1%, 5%, and 10%, respectively.

. The results show that there was a bidirectional Granger causality between the GCI and ER and a unidirectional Granger causality with ACE, which verifies the finding of the above-described analysis that green consumption and the degree of ER interact. A two-way Granger causality was found to exist between ER and ACE: ER will effectively inhibit carbon emissions and lower carbon emissions will similarly reduce ER input; the interaction between the two was evident. Green consumption is a one-way Granger cause for the CSCV, suggesting that human behavior has a significant unilateral impact on ecosystems. Furthermore, ER is a one-way Granger cause of vegetation’s ability to sequester carbon because ER policies have a protective effect on the ecological environment. Based on this paper’s analysis of impulse response, we found that the impact of ER on the CSCV was very small in the short term; however, the implementation of ER policies over a longer period in tandem with vegetation’s self-healing abilities, the positive effect of ER on the CSCV will gradually strengthen with time.

4.7. Variance Decomposition Analysis

By analyzing the contribution of each variable and identifying the relative importance of each shock variable to other variables, the variance decomposition helps to analyze the interaction between green consumption, environmental regulation, and carbon emissions and the degree of influence. The forecast periods 2, 4, and 6 are selected to explore the linkages among green consumption, environmental regulations, and carbon emissions. The test results are listed in

Table 9. Variance decomposition of the PVAR models.

GCI, ER, and ACE					GCI, ER, and CSCV
Variable	Period	GCI	ER	ACE	Variable	Period	GCI	ER	CSCV
GCI	2	0.959	0.035	0.006	GCI	2	0.983	0.016	0.001
	4	0.777	0.194	0.029		4	0.913	0.073	0.014
	6	0.592	0.359	0.049		6	0.801	0.174	0.025
ER	2	0.055	0.942	0.003	ER	2	0.031	0.966	0.002
	4	0.027	0.961	0.011		4	0.065	0.901	0.034
	6	0.028	0.955	0.016		6	0.067	0.863	0.070
ACE	2	0.019	0.187	0.795	CSCV	2	0.058	0.015	0.927
	4	0.030	0.379	0.591		4	0.086	0.025	0.888
	6	0.059	0.492	0.449		6	0.087	0.035	0.878

:

(1)

Regarding changes in the GCI, the results of the two models showed that the variance contribution was above 50%, indicating that green consumption innately has a degree of inertia and significant self-enhancement bias; these characteristics will gradually weaken over time. On the other hand, the gradually strengthening influence of ER will be accompanied by the weakening influence of green consumption;

(2)

The contribution rate of ER to its variance was found to be above 85%, indicating that more than 85% of changes in ER can be attributed to itself, probably because ER has a significant level of path dependence, which weakens the explanation of the other variables;

(3)

The contribution of ACE to its variance was found to be above 40%. The variance contribution tended to decrease over time, while the contribution of the variance from Ers gradually increased over time, indicating that the influence of Ers on carbon emissions will strengthen over time;

(4)

The variance contribution of the CSCV to itself was above 85%, and the variance contribution from green consumption and ER was relatively small, indicating that the CSCV has an innate degree of inertia and a stronger self-enhancement effect than ER.

5. Conclusions

Using a PVAR model, this study investigated the connections between green consumption, environmental laws, and carbon emissions. The key findings are presented as follows. (1) The level of green consumption in the chosen sample exhibited a trend of first declining and then stabilizing. (2) Green consumption was found to lower ACE and ER inputs in the short term and to cause the carbon emissions to float around the zero-emission zone in the long term. Green consumption was shown to be significantly and positively impacted by counteraction from ER. (3) This study used the amount of carbon sequestration by plants as another measure of carbon emissions because of the self-absorption effect of carbon emissions, and it was found that the capacity of vegetation to store carbon tends to increase and then decrease as green consumption increases; this effect will become stronger over time.

Based on the findings of this study, we can categorize the effects of environmental regulation on carbon emissions into two main areas. Firstly, the goal is to raise public awareness of environmentally friendly consumption through effective regulatory measures. Secondly, the goal is to reduce carbon emissions by encouraging greener production and lifestyle choices from an ideological standpoint. However, because environmental regulations are weak, these measures take time to take effect. On the other side, by controlling the preservation of ecological vegetation and boosting the vegetation’s capacity to absorb carbon dioxide, the government can reduce carbon emissions at the source [35].

To increase the level of green consumption in China, various strategies can be enacted: Regarding residential production modes and lifestyles, residents can try to choose shared bicycles and new energy vehicles as their main modes of travel. Regarding societal values, the government should vigorously advocate for green consumption, guide individuals to establish correct consumption concepts and cultivate green and low-carbon awareness. The results of our spatial distribution study demonstrate that environmental regulation has an enormous impact on the level of green consumption. Reasonable regulatory policy formulation will result in effective green consumption in a short amount of time. In addition, as natural vegetation has a long action period and takes a long time to recover after being damaged, it is important to consider the sustainability of its carbon sequestration effects. Natural vegetation must therefore be protected.

We think that this paper still needs to be improved in a few areas after combining the findings with the body of existing literature: The data years chosen for this paper are shorter: (1) the data for individual indicators is not available; (2) the majority of the green consumption indicators are based on the authors’ subjective personal understanding and the body of existing literature; and (3) the environmental regulation in this paper is more general due to space constraints. The study’s findings will be more comprehensive and striking if they are more carefully classified into guiding, binding, and incentivizing environmental regulation.