The Impact of New-Type Urbanization on Carbon Emissions—A Case Study of China Based on the Moderating Role of Forest Quality

Abstract

As cities continue to expand, the role of forests in mitigating carbon emissions during urban growth has become a critical concern for both researchers and policymakers. This study constructs a comprehensive framework to assess new-type urbanization and forest health, calculates relevant metrics, and applies the Environmental Kuznets Curve model to examine how contemporary urbanization affects carbon emissions while accounting for the moderating role of forest quality. The results indicate that the impact of urbanization on carbon emissions generally follows an inverted U-shaped pattern, although significant regional variations exist. Forest quality has not yet fully realized its potential in reducing carbon footprints, largely due to the need for overall improvement in the forestry sector. In terms of how urbanization affects forest quality, traditional factors such as population migration and industrial restructuring remain the primary drivers. There is a discernible tension between conventional urban expansion and sustainable forestry development. Although modern urbanization and forest quality show promising synergies, both are constrained by their current developmental stages, which limits their effectiveness in substantially curbing carbon emissions.

1. Introduction

Urban development is integral to social progress. On the positive side, it facilitates industrial upgrading and economic agglomeration. Knowledge and technological spillovers generated within economic hubs enhance local talent capacity and innovation capability, which in turn can contribute to carbon emission reduction. Meanwhile, urban living improves residents’ quality of life and raises environmental awareness. Growing public demand for sustainable development has prompted governments to implement stricter environmental regulations and accelerate the transition toward green, low-carbon practices. However, while traditional urbanization has driven economic growth, it has also increased pressure on infrastructure and stimulated the expansion of energy-intensive industries. Natural resources are being consumed at an alarming rate, exacerbating environmental degradation and potentially intensifying social conflicts. How, then, does urbanization precisely affect carbon emissions? The interplay among socioeconomic factors, technological advancement, and policy interventions remains a subject of ongoing and active academic debate [1,2,3,4,5,6,7].

Urbanization exerts dual effects on carbon emissions—it can both increase and inhibit them—and the relationship between the two is often nonlinear. A complex, bidirectional interaction exists: urbanization shapes regional carbon emission levels through mechanisms such as rising energy consumption, industrial restructuring, and technological advancement. Conversely, carbon emission constraints may impede high-quality urban development, resulting in a long-term dynamic equilibrium between the two. Regarding the specific mechanisms through which urbanization affects carbon emissions, scholarly views diverge. Some studies support a linear relationship, positing a straightforward positive or negative correlation, while others identify nonlinear patterns, such as inverted U-shaped or U-shaped curves.

Scholarly views on the specific impact of urbanization on carbon emissions vary significantly. Proponents of the “linear theory” argue that urbanization and carbon emissions move in tandem, rising and falling together. Other researchers, however, have identified more complex nonlinear patterns, such as inverted U-shaped or U-shaped relationships, suggesting that the effect of urbanization on emissions is not constant but evolves with the stage of development. As urban expansion continues, a growing body of literature contends that this trend will lead to higher carbon emissions [8]. Continued growth of the urban population sustains—and often increases—energy demand for household and economic activities, resulting in a marked rise in carbon dioxide emissions. This not only accelerates environmental degradation but also places considerable strain on the carrying capacity of urban ecosystems, challenging the sustainable development of cities [9,10].

With the deepening development of urbanization, production factors such as labor and capital form agglomeration effects in space, greatly promoting production scale expansion and industrial structure optimization and upgrading through economies of scale and specialized division of labor, thereby becoming the core driving force for regional economic growth [5]. This drives the continuous expansion of energy-intensive industries such as chemicals and construction, causing a sharp increase in carbon dioxide emissions. Meanwhile, “urban diseases” such as traffic congestion, environmental pollution, and ecological degradation continue to intensify, posing severe challenges to the sustainability of urban development [11]. Higher energy use and carbon emissions are also caused by urban consumption patterns [12]. The concentration of people and economic activity in major cities exacerbates energy demand and emissions from transportation, entertainment, and housing [13].

Subsequent research indicates that less urbanization is required for higher carbon. Reducing greenhouse gas emissions can help cities improve their environmental quality [14]. Eco-friendly infrastructure can reduce emissions, according to a substantial body of qualitative and quantitative research [15]. Researchers have examined a nonlinear correlation between urbanization and carbon emissions [16]. They discover that gases evolve non-linearly in the early stages. However, the rate of increase in emissions will decrease after a certain degree of urbanization [17]. Emissions seem to decrease with growing size, and cities seem to follow this reasoning [18].

The relationship between urbanization and carbon emissions exhibits significant characteristics of complexity, diversity, and dynamism. A comprehensive understanding of this relationship requires expanding the spatiotemporal scale of research and conducting integrated investigations across broader regional scopes and longer time spans [19,20,21]. In scientific research and social practice, attention has been directed toward pathways that can achieve coordinated development of urbanization and carbon emissions, leading to the proposal of the new-type urbanization development concept [22]. In summary, new-type urbanization can be defined as a development model that fully embeds the concepts and principles of ecological civilization throughout the entire urbanization process. It follows a pathway marked by intensification, intelligence, greening, and low-carbon emission. This model advocates for the establishment of a scientific and rational urban framework, in which the spatial layout of cities—of all sizes—as well as towns and urban clusters, is closely coordinated with regional economic growth and industrial distribution, while also respecting the carrying capacity of local resources and the environment. Moreover, new-type urbanization treats the orderly integration of the agricultural migrant population into urban society as a key task, embodying its human-centered orientation.

New-type urbanization construction emphasizes high-quality development, and new-type urbanization serves as an important pathway for achieving carbon emission reduction and mitigating global climate change [23]. Clarifying the relationship between carbon emissions and new-type urbanization is conducive to providing a theoretical basis for formulating relevant policies in various regions [24]. Against this backdrop, this study is dedicated to addressing the following key questions: First, does new-type urbanization possess carbon emission reduction effects? Second, what are the transmission pathways through which new-type urbanization affects carbon emissions? Third, do regional disparities exist in the carbon emission effects of urbanization? Based on the research conclusions addressing these questions, this study aims to provide precise policy recommendations for coordinating the advancement of new-type urbanization construction and the synergistic achievement of carbon emission reduction goals.

1.1. Impact of Urbanization on Carbon Emissions

Urbanization is an inevitable trend of social development. The impact of urbanization on carbon emissions has gradually drawn the attention of scholars, but there are relatively few studies on the mechanism of the impact of urbanization on carbon emissions. The current research on the impact mechanism of urbanization on carbon emissions mainly analyzes from the perspectives of energy consumption, industrial structure, technological progress, and agglomeration effects [25,26]. The urbanization process is accompanied by large-scale population migration and a high concentration of economic activities such as industry, construction and transportation, which to a certain extent leads to an increase in regional carbon dioxide emissions [27]. The negative environmental effects brought about by traditional urbanization have led people to start thinking about how to achieve the coordinated development of urbanization and the environment, and thus the new type of urbanization has come into the view of researchers.

Scholars mainly have two views on the relationship between urban modernization and carbon emissions. According to some scientists, contemporary urban development can lower greenhouse gas emissions. Through increased industrial efficiency and improved economic structure, an effective urban management plan can reduce emissions and prevent “rebound effects” [28]. Cities that have embraced modern development concepts have significantly fewer carbon emissions than conventional cities. Urban policy ought to be “adapted to local conditions” as a result [29]. In addition, both new urban construction and low-carbon urban design have obvious “spatial spillover effects”, and, especially when economic development and environmental protection goals are consistent, this carbon reduction effect is stronger [30]. The evolution of modern cities has also reduced carbon emissions in the construction industry, mainly due to industrial progress and the creation of an ecologically friendly urban environment [31]. Finally, the growing interdependence between new urbanization and transportation emission efficiency is becoming a key factor in our transition to a low-carbon economic model [32].

1.2. Relationship Between Urbanization and Forestry

Forests are a major part of terrestrial ecosystems, and they provide important services such as managing water and protecting soil. Forestry relying on these resources is not only the foundation of ecology, but also an important economic source for environmental protection projects. In addition, the development of forestry helps expand forest area, protect ecosystems, and stimulate economic activities. This industry forms a complete economic network, has an extensive supply chain, and plays a leading role in carbon sequestration and emission reduction [33,34,35].

Although the environmental burden of the forest products industry is usually lighter than that of other manufacturing industries, some developing countries like China still generate a lot of carbon emissions in this area [36]. Being a secondary processing of forestry and one of the most energy-intensive businesses, the papermaking sector is intriguing [37]. China’s rapid industrialization and urbanization are intertwined and lead to the acquisition of numerous resources. Forest growth is also improving as a result of the fast rise of metropolitan areas [38].

Due to the constantly expanding consumer market, urbanization has had an impact on forestry [39]. Future growth in the timber business may result in a significant rise in carbon emissions. China’s traditional urbanization approach is encountering more and more challenges. Urbanization raises the danger of climate change and increases carbon emissions, among other human-caused changes in land use [40,41,42].

The impact of urban expansion on carbon emissions is currently being intensively examined by researchers. They are examining how the expansion of cities can result in the preservation of forests. The influence of water systems and vegetation on city climate-related problems will also be evaluated. Discoveries and research techniques continue to emerge in the field, despite the fact that no consensus has yet been achieved. There are numerous connections between urbanization and carbon emissions. Changes in land use and energy consumption may be the direct causes of the latter. Similarly, they could be indirectly brought on by changes in industry and ways of living. A thorough understanding of these connections is necessary to develop urban climate policies.

Urban trees have a significant and sometimes disregarded impact in reducing global warming. They are crucial carbon sinks that lower emissions brought on by cities because they absorb the CO₂ in the atmosphere. Urbanization causes the loss of natural plant cover, according to research on the capacity of urban vegetation to sequester carbon. However, cities can boost their potential through afforestation expansion and planning [43]. According to research, China’s urban forests’ carbon stocks have changed dramatically as a result of the country’s fast urbanization process. Without a doubt, urban forests will contribute to a reduction in pollutants and urban heat [44].

The degree to which urbanization alters land use and land cover varies, and its effects on urban forests are not all the same. Researchers are becoming more interested in how these changes affect the carbon balance of ecosystems. The expansion of urbanization takes up agricultural land. However, there are substantial greenhouse gas emissions when agriculture is replaced with built-up land [45]. Cities have become warmer and require more energy due to the urban heat island effect. In light of global warming, this effect is now significant. Additionally, there are some repercussions of urbanization, such as a rise in the frequency of extreme weather patterns. Urban growth’s causes, procedures, locations, timing, and environmental effects have all been extensively researched [46].

Urban development-induced land use changes typically result in significant carbon exchange, second only to the burning of fossil fuels [47]. Researchers are becoming more interested in how urban land use patterns, particularly forest cover, impact carbon sequestration and other environmental services [48]. Therefore, promoting urban forests and enhancing their ecological services is crucial from both theoretical and practical perspectives.

In summary, traditional urbanization is a development process characterized by population growth, city building, and industrial development. Traditional urbanization has restricted sustainable forestry by causing deforestation, land reclamation, and water source redistribution. New-type urbanization is a people-centered approach that considers ecosystem protection, especially for forestry and water resources, during development.

2. Data and Methods

2.1. Data Source

The data used in this study constitutes a balanced provincial panel. All indicators are collected at the annual frequency for the period 2009–2023, covering 31 provincial-level administrative regions in mainland China (including 22 provinces, 4 municipalities, and 5 autonomous regions). This paper uses the provincial panel data of CO₂ emissions from China Emission Accounts Datasets (CEADs, 2009–2023) [49]. The carbon emission values calculated in the CEADs refer to direct carbon emission data, mainly based on heterogeneous energy consumption data of various provincial regions in China combined with energy carbon emission coefficients to measure direct carbon emissions. Therefore, this manuscript mainly dis-cusses the issue of direct carbon emissions. The urbanization and forestry quality data for this study primarily comes from the China Statistical Yearbook (2009–2023). Socio-economic and forestry data from the China Statistical Yearbook are official and widely used in related research.

The calculation of provincial carbon emissions in CEADs is based on the emission factor method, where the emission coefficients are derived from the officially published reference methods and corresponding emission factor values by the Intergovernmental Panel on Climate Change (IPCC). The calculation formula is shown in Equation (1).

$$EC={\sum }_{m=1}^8{EC}_m={\sum }_{m=1}^8{E}_m\times {\mathsf{\Psi }}_m\times {\rho }_m$$

(1)

Here, m represents the type of energy. This study selects eight major energy sources: coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, and natural gas. Their consumption is converted into 10,000 tons of standard coal equivalent (tce). The consumption data for each energy type by province are sourced from the China Energy Statistical Yearbook. Since the officially published consumption data are provided in physical units, the first seven types are measured in 10,000 tons, while natural gas is measured in 100 million cubic meters. The consumption of different energy types needs to be uniformly converted into 10,000 tons of standard coal equivalent (tce), and then the carbon emissions from the combustion process of each energy type are calculated accordingly. The standard coal conversion coefficient $\psi$ and the carbon emission coefficient $\rho$ are used for this conversion. The standard coal conversion coefficients and carbon emission coefficients, as shown in

Table 1. Standard coal conversion coefficients and carbon emission coefficients for major energy types.

Energy Type	Coal	Coke	Crude Oil	Gasoline	Kerosene	Diesel	Fuel Oil	Natural Gas
Std. Coal Conv. Coefficient	0.7143	0.9714	1.4286	1.4714	1.4714	1.4571	1.4286	1.3300
Unit	kg/kg	kg/kg	kg/kg	kg/kg	kg/kg	kg/kg	kg/kg	kg/m3
Carbon Emission Coefficient	0.7559	0.8550	0.5857	0.5538	0.5714	0.5921	0.6185	0.4483
Unit	t/t	t/t	t/t	t/t	t/t	t/t	t/t	t/t

, are obtained from the China Energy Statistical Yearbook and IPCC (2006).

The urbanization and forestry quality data for this study mainly comes from the China Statistical Yearbook (2009–2023), as shown in

Table 2. Data sources.

Evaluation System	Primary Indicator	Secondary Indicator
Urbanization	Demographic	Urban population proportion
	Economic	GDP per capita
		Share of secondary and tertiary industries in GDP
	Social development	Health technicians per 1000 people
		Expenditure on education
	Science and technology	Number of patents granted
	Urban construction	Investment in real estate enterprises
	Pollution control	Investment in industrial pollution control
Forestry quality	Economic	Forestry Output Value
		Forestry Investment
	Area	Forest Land Area
		Forest Area
	Ecological	Forest Pest Control Rate
	Total standing tree count	Forest Growing Stock

Data source: China Statistical Yearbook (http://www.stats.gov.cn/sj/ndsj/ (accessed on 1 October 2025)).

.

2.2. Methods

A variety of established methods are employed by researchers to identify the sources of CO₂ emissions. Among the more widely used approaches are the Environmental Kuznets Curve (EKC) hypothesis, the Kaya identity, the Logarithmic Mean Divisia Index (LMDI) decomposition method, and the STIRPAT model.

The EKC hypothesis examines the relationship between environmental quality and economic development, proposing an inverted U-shaped association between pollution and income level—wherein pollution initially rises with economic growth but declines after a certain income threshold is reached [49,50].

The Kaya identity expresses carbon emissions as the product of four key factors [51] and is commonly applied to analyze how changes in these drivers influence emissions over time [52]. This decomposition helps illustrate how long-term shifts in different factors shape emission trends.

While time-series techniques are frequently used in the literature to assess the relationship between CO₂ emissions and GDP changes [53,54,55,56], such methods often cannot precisely identify the underlying sources or directional impacts of emissions. These limitations underscore the need for more integrated analytical frameworks to better understand how urbanization influences emission patterns [57].

LMDI decomposition is recognized for its comprehensiveness and accuracy [58,59,60,61]. It disentangles total energy consumption into individual components and evaluates their respective contributions to changes in consumption [62,63].

The STIRPAT model, adapted from an earlier conceptual framework [64], is often tailored by researchers to investigate specific relationships—such as the effect of economic growth on environmental outcomes [65].

Among the related methods, EKC is by far the most used. This framework provides a powerful analytical tool for studying the relationship between economic growth and environmental quality [66,67]. The central idea of this theory is that environmental deterioration usually occurs in conjunction with income growth at the beginning of economic development. However, when incomes exceed a certain level, environmental conditions begin to improve and will get better and better as the economy continues to develop.

There are several reasons for this. In the early stages of development, people were most concerned about meeting basic survival needs. Once these material needs are met, people will increasingly want a better quality of life and care more about the surrounding environment. Fundamentally speaking, economic development is the continuous improvement and upgrading of the economic structure. This usually shifts from agriculture-oriented to industry-oriented, and finally shifts to service-oriented. Considering that heavy industry requires a large amount of resources, while the energy demand of the service industry is relatively low, the relationship between economic growth and energy consumption will take on an inverted U-shape.

Economic development will lead to greater resource extraction, which will cause pollution problems. At the same time, economic development can also stimulate technological innovation, increase R&D spending, and improve energy efficiency. These factors add up to ultimately determine where environmental quality will go.

Scholars have extended the EKC framework by adding economic structure and market dynamics [68,69,70], arguing that urban development also changes environmental conditions and should be included in the EKC analysis. They proposed that there is an inverted U-shaped relationship between urbanization and ecosystems. Theories like urban environmental transition theory, ecological modernization theory, and compact city methods all help us understand how urban growth affects carbon emissions. Urban environmental transition theory holds that large-scale population migration from rural areas to cities, and subsequent population growth in cities, will increase the demand for public services and infrastructure [71]. In addition, population density will make transportation problems worse and increase energy use, making carbon emissions worse. But in turn, urban development can also make the public want a clean environment and encourage companies to adopt more sustainable practices. Therefore, the ultimate impact of urbanization on carbon emissions depends on which of these opposing effects is stronger [72].

The theory of ecological modernization emphasizes that different stages of socio-economic development have varying impacts on the regional ecological environment. In the early days of social progress, it was characterized by low urbanization, high energy consumption of residents, and environmental degradation usually aggravated. However, with the advancement of regional economy and technology, urbanization helps spread knowledge and innovation through industrial transformation and energy system upgrades, which in turn reduces local carbon emissions [73,74]. Compact city theory focuses on the positive impacts of urbanization. It maintains that a site that is both an excellent location and has a higher urban density can help draw in economic activity. This clustering makes it possible to make the most use of common resources, especially pollution-control systems, which results in economies of scale that improve environmental quality and drastically lower carbon emissions.

We may observe that there are advantages and disadvantages to some of the popular research methodologies. We may use the Kaya identity to break down components over time. Maintaining consistency with Kaya’s output is challenging. Although the LMDI decomposes without residuals, it is unable to estimate various driving forces in an elastic manner. The most thorough methods for evaluating carbon emissions are referred to by the STIRPAT framework.

The EKC framework’s application potential is significantly greater due to its universality and simplicity. Using common statistical methods, such as parameter estimation and data validation, the model produces accurate results that aid in a numerical evaluation of emissions causes. The aforementioned theories suggest that the relationship between urbanization and carbon emissions is likely non-linear and that there are certain barriers to this relationship.

According to the EKC model and the literature, the research assumptions are as follows: New-type urbanization and forestry quality have a major impact on carbon emissions. There is an inverse U-shaped relationship between carbon emissions, forestry quality, and new-type urbanization. Regional differences exist in the impact of forestry quality and new-type urbanization on carbon emissions. When it comes to cutting carbon emissions, new-type urbanization works better than traditional urbanization.

In order to verify the research assumptions mentioned above, we will do this research step by step: First, we will build an evaluation system for new-type urbanization and forestry quality, and then use the entropy weight method to calculate the urbanization level and forestry quality of various provinces in China. Next, we will create two Environmental Kuznets Curve models-one to see how new urbanization affects carbon emissions, and the other to see how urbanization and forestry quality together affect carbon emissions-so that we can understand the relationship between them. Then, we will use the STIRPAT model to find specific ways in which forestry quality affects carbon emissions. Finally, based on the research results, we will give some useful policy suggestions to let everyone understand the use of this research.

As mentioned above, based on relevant literature [50], this study directly presents the EKC model form (1) for the impact of urbanization on carbon emissions [66,67,68,69,70,71,72,73,74].

ln C = α₀ + α₁U + α₂U² + e

(2)

where C represents environmental pressure, measured by total regional carbon emissions; U denotes the new-type urbanization index; e is the random disturbance term; and α₀ is the constant term. By incorporating the forestry quality indicator as a control variable into Model (1), Model (2) is obtained:

ln C = α₀ + α₁U + α₂U² + α₃F + e

(3)

where F represents the comprehensive index of regional forestry quality. The urbanization index evaluation indicator system and the forestry quality evaluation system are shown in

Table 3. Urbanization and forestry quality evaluation system.

Evaluation System	Primary Indicator	Weight
Urbanization	Demographic	0.153
	Economic	0.140
		0.157
	Social development	0.152
		0.118
	Science and technology	0.070
	Urban construction	0.106
	Pollution control	0.103
Forestry quality	Economic	0.163
		0.169
	Area	0.175
		0.173
	Ecological	0.174
	Total standing tree count	0.146

.

This study analyzes the impact effects of forestry quality on urbanization-related carbon emissions through forestry transmission mechanism analysis. The following model (4) presents the EKC transmission mechanism model of urbanization’s impact on forestry quality.

ln Fi =β₀ + β₁U + β₂U² + e

(4)

The foregoing analysis investigates the impact of the urbanization index on forestry quality elements. To perform a more granular analysis of the transmission mechanisms through which urbanization factors affect forestry quality elements, this study adopts the STIRPAT modeling framework for model construction and regression analysis [64,65]. The empirical results are presented in the table below. The model is specified as follows:

ln Fi = γ₀ + γ₁ ln U₁ + γ₂ ln U₂ + γ₃ ln U₃ + γ₄ ln U₄
+ γ₅ ln U₅ + γ₆ ln U₆ + γ₇ ln U₇ + γ₈ ln U₈ + e

(5)

The symbiotic relationship between urbanization and forestry can be conceptualized in two complementary dimensions. On one hand, forestry resources contribute to new-type urbanization in multiple ways: as a natural resource, forests serve as ecological barriers and provide environmental purification; economically, the forestry sector supplies raw materials and creates employment opportunities to support urban growth; socially, forest-derived ecological products fulfill urban residents’ growing demand for high-quality living environments. On the other hand, new-type urbanization positively influences the enhancement of forestry quality. Unlike traditional urbanization, it emphasizes ecological civilization and strives to minimize harm to forestry and forest resources. Furthermore, well-developed new-type urbanization can channel more advanced technologies, adequate funding, skilled professionals, and improved equipment into forestry, thereby fostering its qualitative development. To quantify the level of symbiotic development between new-type urbanization and forestry quality, this study adopts a coupling degree index [75,76]. The coupling intensity coefficient is given as follows:

$$c=2\times {\left[{\displaystyle \frac{U\cdot F}{{\left(U+F\right)}^2}}\right]}^{{\displaystyle \frac{1}{2}}}$$

(6)

3. Results

The urbanization index evaluation indicator system and the forestry quality evaluation system are shown in Table 3.

The forest quality indicator system comprises ecological, social, and economic dimensions [77,78]. This paper uses the provincial panel data of CO₂ emissions from China Emission Accounts Datasets (CEADs) [79]. Data on urbanization and forests in provincial-level regions of China from 2015 to 2023 have been selected from the China Statistical Yearbook. The entropy weights of the different indicators will then be measured. The entropy weight method increases objectivity [80].

The multiple correlations between variables were analyzed through panel data, as shown in

Table 4. Statistical characteristics of new urbanization and forestry quality index.

Statistics	lnC	U	F
Mean	5.631	0.330	0.691
Standard Error	0.044	0.007	0.009
Median	5.632	0.305	0.726
Standard Deviation	0.724	0.116	0.153
Sample Variance	0.525	0.013	0.023
Kurtosis	0.425	−0.059	1.275
Skewness	−0.641	0.820	−1.327
Range	3.214	0.512	0.660
Minimum	3.676	0.143	0.243
Maximum	6.890	0.655	0.903
Sum	1520.410	89.027	186.468
Count	450	450	450

. The average value of the variance expansion factor for the outcome variable is 3.77, which is required for composite carbon. Meanwhile, after conducting Hausman tests on fixed effects and random effects models, it was found that the fixed effects model is applicable to this study.

3.1. Baseline Regression

In panel data analysis, this study tested the multiple correlation of variables and selected the variance expansion factor (VIF) method for testing (as shown in

Table 5. Variance inflation factor test.

Variable	VIF	1/VIF
lnC	2.55	0.39
U	4.66	0.21
F	2.12	0.47
Mean VIF	3.27

). The average value of variance expansion factor of variables in the model is 4.44, meeting the data requirements of panel regression. At the same time, after conducting Hausman test fixed-effects and random-effects models, it is found that the fixed effects model is suitable for this study.

To verify the impact of new-type urbanization on carbon emissions, this study first conducts regression analysis using the EKC benchmark model for carbon emissions of new-type urbanization. The regression results are shown in

Table 6. Benchmark Model Regression and Robustness Test for Basic EKC model.

Sample Time Span	α0	α1	α2	Shape of the Curve
2015–2023	2.833 ***	15.256 ***	−18.281 ***	inverted U-shape
2019–2023	2.289 ***	17.239 ***	−19.618 ***	inverted U-shape
2018–2022	2.455 ***	16.940 ***	−20.027 ***	inverted U-shape
2017–2021	2.573 ***	16.819 ***	−20.618 ***	inverted U-shape
2016–2020	2.330 ***	19.144 ***	−24.869 ***	inverted U-shape
2015–2019	2.410 ***	18.974 ***	−24.896 ***	inverted U-shape

*** p value < 0.01.

. To test the robustness of the model, this study performs robustness analysis by changing the sample size. The regression results of sample data across different time spans indicate that the model exhibits good robustness.

Table 6 shows that the impact coefficient of China’s new urbanization on carbon emissions is positive, which shows that our country’s urbanization has generally led to more carbon emissions. Moreover, the relationship between urbanization and carbon emissions is like an inverted “U” shape-carbon emissions will increase at first and only decline after reaching a certain point. Because urbanization levels, social progress and environmental protection measures vary greatly among provinces in China, this study also investigated regional differences in the impact of new urbanization on carbon emissions. The results of the model analysis are shown in

Table 7. Regional Heterogeneity in the Impact of Urbanization on Carbon Emissions.

Region	α0	α1	α2	Shape of the Curve
North China	10.405 ***	−15.561 ***	9.299 **	U-shape
Northeast China	7.431 ***	−16.903 ***	37.589 ***	U-shape
East China	3.467 ***	11.630 ***	−12.713 **	inverted U-shape
South-Central China	1.357 ***	21.485 ***	−22.249 ***	inverted U-shape
Southwest China	4.602 ***	7.154 *	−14.305 *	inverted U-shape
Northwest China	7.817 ***	−23.267 *	48.501 *	U-shape
High-level urbanization	5.960 ***	1.709 ***	−4.163 **	inverted U-shape
Low-level urbanization	2.309 ***	20.538 ***	−31.399 ***	inverted U-shape

* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

.

As shown in Table 7, the impact mechanisms of urbanization on carbon emissions in East China, Central-South China, and Southwest China resemble the national pattern, with their Environmental Kuznets Curves (EKC) all exhibiting an inverted U-shape. In contrast, northern regions such as North China, Northeast China, and Northwest China display a U-shaped EKC relationship, suggesting greater challenges in curbing carbon emissions. These areas require improvements in industrial structure adjustment and the protection of the natural ecological environment.

Although urbanization can yield economic benefits, its environmental effects vary significantly depending on regional development levels and industrial characteristics. As urbanization progresses, economic zones often undergo structural shifts toward service-oriented industries, which generally emit less carbon than manufacturing sectors. Regions with more advanced legal and technological frameworks are better equipped to manage pollutants and promote cleaner production. Furthermore, mandatory carbon emission reporting can incentivize firms to adopt low-carbon technologies and improve production efficiency, thereby helping to slow the growth rate of carbon emissions.

Conversely, less developed areas frequently rely on wasteful resource use and ineffective industrial growth to propel their economies. With GDP growth as the top goal, local governments frequently adopt a growth-first strategy, permitting—and occasionally even encouraging—dirty and energy-intensive companies. The increase in carbon emissions and the modernization of heavy industries in real estate and infrastructure

Carbon emissions are significantly impacted by a city’s size. It is easier for people to share hospitals, schools, and public transportation in large cities. People may share ideas more readily thanks to platforms like this one. Better economic and environmental performance results from this trade. Cities benefit from current knowledge and green technologies that lower carbon footprints and promote cleaner output.

The infrastructure needed to support their expanding population is lacking in many small settlements. The region’s infrastructure development uses carbon-intensive items like steel and coal, which exacerbates the emissions issue. The environment is impacted by people’s educational attainment. Innovative technologies are produced and the talent pool is diversified by having a highly educated and talented workforce. This significantly lowers carbon emissions and advances the development and application of cleaner industrial methods. Because they have less access to technology and creative spaces, those with lower levels of education are typically ineffective at reducing carbon emissions.

3.2. The Impact of Forestry on Urbanization-Related Carbon Emissions

Urbanization has a complicated and situation-specific impact on carbon emissions. Trees are essential for absorbing toxic gasses such as nitrogen oxide, sulfur dioxide, and ammonia. Thus, the analysis of the carbon footprint of urbanization is expanded to include issues related to forests. Earlier research frequently used simple statistics, such as “the total area of forest” or “the total yield.” This time, though, we prioritized quality and created a framework for a comprehensive evaluation of the state of forests. An Environmental Kuznets Curve was created using the aforementioned enhanced indicator to quantify the combined effects of forest quality and urbanization on carbon emissions.

Table 8. Benchmark Model Regression and Robustness Test for Expanded EKC model.

Sample Time Span	α0	α1	α2	α3	Shape of the Curve
2015–2023	1.249 ***	13.663 ***	−14.747 ***	2.430 ***	inverted U-shape
2019–2023	0.455 *	16.725 ***	−17.577 ***	2.469 ***	inverted U-shape
2018–2022	0.714 *	15.666 ***	−16.847 ***	2.521 ***	inverted U-shape
2017–2021	0.970 *	14.936 ***	−16.508 ***	2.489 ***	inverted U-shape
2016–2020	0.909 *	16.130 ***	−18.824 ***	2.473 ***	inverted U-shape
2015–2019	1.048 **	15.839 ***	−18.624 ***	2.434 ***	inverted U-shape

* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

displays the particular outcomes.

Table 8 shows that across China, the impact coefficient of forestry quality on carbon emissions is positive, and carbon emissions have not been reduced as expected. There are many reasons for this result, which may involve some transfer and coupling processes and require more detailed study.

Regional differences in forestry quality are significant. The first is that each location has a unique natural geographical setting, which affects the quality of its forests. The second reason is that each region has a varied level of social and economic development, which influences how forestry resources are allocated and managed, resulting in varying forestry quality development. In light of this, we also evaluated the relationship between carbon emissions and urbanization throughout the different EKC regions, while included forest quality as a controlling factor.

Table 9. Regional Differences in the Impact of Urbanization on Carbon Emissions with Forestry Quality.

Region	α0	α1	α2	α3	Shape of the Curve
North China	7.203 ***	−5.653 *	−0.462 *	1.490 ***	linearly decreasing
Northeast China	8.467 ***	−19.968 ***	41.875 ***	−0.690 *	U-shape
East China	2.722 ***	7.730 *	−6.043 *	1.687 ***	inverted U-shape
South-Central China	−0.046	13.242 ***	−13.188 ***	4.124 ***	inverted U-shape
Southwest China	3.644 ***	3.319 *	−5.951 *	1.694 ***	inverted U-shape
Northwest China	3.980 *	−4.827 *	14.151	2.361 ***	U-shape
High-level urbanization	7.377 ***	5.180 **	−5.918 *	−3.075 ***	inverted U-shape
Low-level urbanization	3.989 *	2.903 ***	−3.697 ***	1.516 ***	inverted U-shape

* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

displays the analysis’s findings.

The results presented in Table 9 indicate that in most regions, forestry quality exhibits a positive coefficient in relation to carbon emissions, implying it does not contribute to emission reduction. Regional analyses further reveal notable spatial heterogeneity. For instance, Northeast China is endowed with abundant and high-quality forest resources. In such regions, forest quality significantly influences carbon emissions, primarily because forests serve as carbon sinks. Correspondingly, experiments stratified by forestry quality levels confirm that areas with superior forest quality can effectively reduce carbon emissions. In contrast, forestry quality in North, East, Central-South, Southwest, and Northwest China shows no discernible effect on lowering carbon emissions. The underlying drivers of these divergent outcomes warrant further investigation through an analysis of the transmission mechanisms linking forestry quality and carbon emissions.

It should also be noted that the forestry development process itself generates carbon emissions. Existing studies indicate that economic development and labor supply are primary factors influencing carbon emissions in the paper industry, while carbon intensity, energy intensity, industrial structure, social wealth, and population size also significantly affect industrial carbon emissions. These factors similarly play important roles in the carbon emissions associated with the forest industry.

Building on the preceding analysis, this study refines the variable selection in modeling the transmission mechanism through which forestry quality affects carbon emissions. Specifically, we introduce variables representing capital stock and household consumption.

Fixed-asset investment not only drives sustained output and carbon emissions over the long term but is also subject to depreciation. To capture both the persistent output effect and the depreciation of fixed assets, while also reflecting the directional influence of new investment, this study uses capital stock in place of annual investment flows as an explanatory variable. This adjustment provides a more accurate representation of the long-term impact of investment on carbon emissions from the forest industry.

Furthermore, forest industry products are directly tied to the consumer market, serving as essential inputs for residential construction and daily life. Accordingly, household consumption is incorporated into the explanatory variable set to elucidate how consumer demand drives carbon emissions in the forest sector. This approach also offers empirical support for the design of consumption-side carbon reduction policies.

3.3. Analysis of Forestry Transmission Mechanisms

To explain the research findings presented above, this study analyzes the impact effects of forestry quality on urbanization-related carbon emissions through forestry transmission mechanism analysis.

Table 10. EKC Relationship Between Urbanization and Forestry Quality: Regional Analysis.

Dependent Variable	β0	β1	β2	Model Specification
Ln forestry output value (ln F1)	5.878 ***	−5.878 *	5.986 *	U-shape
Ln forestry investment (ln F2)	5.447 ***	−4.218 *	4.117 *	U-shape
Ln forest-land area (ln F3)	6.894 ***	−1.888 *	1.014 *	U-shape
Ln forest area (ln F4)	6.894 ***	−3.523 **	2.900 *	U-shape
Ln pest control coverage in forestry (ln F5)	3.847 ***	1.980 *	−1.685 *	inverted U-shape
Ln forest growing stock (ln F6)	10.905 ***	−4.389 *	3.948 *	U-shape

* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

presents the EKC transmission mechanism model of urbanization’s impact on forestry quality.

Table 10 demonstrates the impact mechanism of the comprehensive evaluation factors of new-type urbanization on forestry quality factors, which can explain the impact mechanism of forestry quality on carbon emissions during the urbanization process. The results show that the comprehensive index of new-type urbanization has predominantly negative impacts on forestry quality elements, but the coefficients of the squared terms of most urbanization indices are positive, indicating that the impact of urbanization on forestry output value, forestry investment, forestry land, forest area, and forest stock volume exhibits a U-shaped relationship. This implies that in the early stages of urbanization, due to resource constraints, there is resource competition between urbanization development and forestry development, and urban expansion crowds out forestry development space, exerting a negative impact on forestry quality. However, when urbanization develops to a certain level, with technological progress, improved resource utilization efficiency, and enhanced environmental awareness, the impact of urbanization on forestry quality development transforms into a positive promoting effect. In addition, the impact coefficient of the urbanization index on forestry pest and disease control is significantly positive, indicating that the urbanization process is conducive to improving forestry pest and disease control capabilities.

The foregoing analysis investigates the impact of the urbanization index on forestry quality elements. To perform a more granular analysis of the transmission mechanisms through which urbanization factors affect forestry quality elements, this study adopts the STIRPAT modeling framework for model construction and regression analysis. The empirical results are presented in the table below. The model is specified as follows:

The data in

Table 11. STIRPAT-Based Analysis of Urbanization Factors’ Impact on Forestry Quality Elements.

Explanatory Variables		Dependent Variable
Coefficient	Indicator	Ln Forestry Output Value (ln F1)	Ln Forestry Investment (ln F2)	Ln Forest-Land Area (ln F3)	Ln Forest Area (ln F4)	Ln Pest Control Coverage (ln F5)	Ln Forest Growing Stock (ln F6)
γ0	Intercept	24.609 ***	12.787 **	14.428 *	17.132 **	5.583 ***	27.374 ***
γ1	Urban population share	−2.663 ***	−0.935 *	−0.642 *	−1.465 *	0.811 ***	−2.502 *
γ2	GDP per capita	−0.033 *	−0.096 *	−0.288 *	−0.158 *	−0.256 *	−0.006 *
γ3	Share of non-agricultural output	−2.427 *	−0.847 *	−0.662 *	−0.877 *	−0.606 *	−1.636 *
γ4	Number of health technicians	0.686	0.331	1.221 **	1.221 **	0.047	1.714 ***
γ5	Public education expenditure	−0.053	−0.030	−0.104	−0.131	−0.040	−0.281
γ6	Patent grants	0.150	−0.018	−0.063	−0.027	0.081 **	0.041
γ7	Real estate investment	0.299 **	0.130	0.069	0.132	−0.007	0.199
γ8	Industrial pollution abatement expenditure	−0.170 **	−0.040	0.020	−0.023	0.059 **	−0.032

* p value < 0.1, ** p value < 0.05, *** p value < 0.01.

illustrate the disaggregated factor impact mechanisms. The analysis results indicate that some urbanization factors have significant impacts on forestry quality elements, such as the proportion of urban population, GDP per capita, and the proportion of non-agricultural industries. The proportion of urban population, GDP per capita, and the proportion of non-agricultural industries are traditional urbanization indicators, suggesting that traditional urbanization indicators have significant impacts on forestry quality elements.

The impacts of new-type urbanization factors on forestry quality elements are generally insignificant, indicating that at the current stage, forestry quality development is mainly constrained by traditional urbanization factors, and population agglomeration, economic growth, and industrial structure adjustment during the traditional urbanization process have generated strong crowding-out effects on forestry quality. Specifically, traditional urbanization factor indicators such as population proportion, GDP per capita, and the proportion of non-agricultural industries mainly exert negative impacts on forestry quality indicators. Forest carbon sequestration capacity is influenced by multiple factors including tree species composition, average age, stock volume, natural climatic conditions, and management levels. At the current stage in China, commercial logging of natural forests has been completely banned, and the proportion of newly added plantation area each year is relatively low. Therefore, the tree species structure of forests will not undergo dramatic changes in the next 40 years. The annual afforestation area before 2035 will be basically similar to the annual afforestation area during 2015–2023, with less afforestation during 2035–2060. This means that the enhancement of forest carbon sink capacity will mainly depend on the age growth and stock volume increase of existing forests, rather than the expansion of new forest areas.

3.4. Evaluation of the Coupling Degree Between Forestry Quality and New-Type Urbanization

This study employs the coupling degree index to reflect the symbiotic development level between new-type urbanization and forestry quality [79,80].

Table 12. Measurement Results of the Coupling Degree Between New-Type Urbanization and Forestry Quality.

Provinces	2023	2022	2021	2020	2019	2018	2017	2016	2015	Mean
Beijing	0.999	1.000	1.000	1.000	0.999	1.000	1.000	0.999	0.998	0.999
Tianjin	0.966	0.969	0.990	0.995	0.985	0.989	0.977	0.982	0.945	0.978
Hebei	0.922	0.908	0.919	0.916	0.909	0.880	0.888	0.904	0.897	0.905
Shanxi	0.939	0.938	0.935	0.940	0.933	0.915	0.911	0.916	0.945	0.930
Inner Mongolia	0.919	0.898	0.894	0.892	0.895	0.898	0.900	0.910	0.927	0.904
Liaoning	0.943	0.938	0.936	0.936	0.927	0.917	0.932	0.935	0.959	0.936
Jilin	0.904	0.891	0.876	0.896	0.887	0.871	0.863	0.865	0.890	0.883
Heilongjiang	0.800	0.773	0.739	0.799	0.789	0.797	0.797	0.792	0.837	0.792
Shanghai	0.924	0.943	0.956	0.960	0.957	0.947	0.959	0.957	0.902	0.945
Jiangsu	1.000	0.999	0.999	0.997	0.994	0.989	0.985	0.979	0.987	0.992
Zhejiang	0.992	0.990	0.989	0.986	0.979	0.975	0.971	0.967	0.978	0.981
Anhui	0.943	0.931	0.931	0.915	0.902	0.884	0.864	0.849	0.876	0.900
Fujian	0.953	0.950	0.949	0.934	0.922	0.908	0.910	0.901	0.920	0.927
Jiangxi	0.903	0.891	0.887	0.869	0.856	0.833	0.831	0.829	0.835	0.859
Shandong	0.983	0.973	0.981	0.984	0.976	0.969	0.956	0.963	0.957	0.971
Henan	0.943	0.935	0.952	0.939	0.938	0.917	0.886	0.883	0.873	0.918
Hubei	0.948	0.926	0.931	0.917	0.913	0.901	0.887	0.881	0.901	0.912
Hunan	0.927	0.903	0.905	0.905	0.879	0.854	0.844	0.826	0.836	0.875
Guangdong	0.992	0.990	0.990	0.993	0.981	0.968	0.965	0.960	0.977	0.980
Guangxi	0.856	0.841	0.846	0.826	0.829	0.827	0.817	0.792	0.784	0.824
Hainan	0.946	0.916	0.922	0.869	0.897	0.855	0.854	0.845	0.868	0.886
Chongqing	0.955	0.949	0.950	0.943	0.937	0.928	0.945	0.920	0.945	0.941
Sichuan	0.903	0.893	0.893	0.879	0.861	0.830	0.820	0.815	0.817	0.857
Guizhou	0.866	0.858	0.851	0.824	0.805	0.781	0.789	0.786	0.792	0.817
Yunnan	0.849	0.840	0.841	0.806	0.786	0.761	0.751	0.734	0.746	0.790
Shaanxi	0.944	0.938	0.939	0.931	0.919	0.904	0.902	0.898	0.909	0.920
Gansu	0.878	0.862	0.854	0.849	0.835	0.807	0.796	0.811	0.815	0.834
Qinghai	0.935	0.930	0.925	0.917	0.914	0.915	0.911	0.924	0.938	0.923
Ningxia	0.993	0.992	0.992	0.990	0.989	0.990	0.986	0.973	0.986	0.988
Xinjiang	0.883	0.866	0.876	0.858	0.847	0.823	0.829	0.832	0.850	0.851

reveals that the coupling degree between new-type urbanization and forestry quality is predominantly high across China. The average coupling degree in most provincial regions surpasses 0.9, with even the provinces exhibiting the lowest values—Heilongjiang and Yunnan—achieving 0.79. These findings suggest that the impediment to carbon emission reduction is not attributable to inadequate coupling and coordination between urbanization and forestry quality, but rather to the intrinsically low levels of both urbanization and forestry quality.

4. Discussion and Policy Recommendations

4.1. Discussion

The empirical findings from the third part of this study provide critical evidence for understanding the complex relationships among new-type urbanization, forest quality, and carbon emissions, which directly informs the following discussion.

The impact of new-type urbanization on carbon emissions exhibits significant regional heterogeneity (corresponding to Table 7). The findings indicate that in East China, Central-South China, and Southwest China, the relationship aligns with the national inverted U-shaped Environmental Kuznets Curve pattern. In contrast, northern regions such as North China, Northeast China, and Northwest China display a U-shaped EKC relationship. This disparity primarily stems from differences in the stages of urbanization, industrial structures, and the stringency of environmental regulations across regions. The more developed eastern and southern regions have entered a later stage of industrialization, characterized by a shift towards service-oriented industries and notable technology spillover effects, which facilitates passing the carbon emission inflection point. Conversely, some areas in the north and west remain in a growth phase dominated by resource extraction and heavy industries, where the urbanization process is accompanied by intense energy demands, leading to continuously rising carbon emissions. This finding corroborates that the environmental impact of urbanization is characterized by stage dependence and path dependence.

Forest quality has not exerted the expected carbon emission reduction effect (corresponding to Table 8 and Table 9). Regression analysis at the national level shows a positive coefficient for the forest quality index, indicating that current forestry development has not, on the whole, suppressed carbon emissions. Regional analysis further reveals that only Northeast China, endowed with exceptionally high-quality forest resources, shows a significant negative impact. This points to a core issue: forest quality in most parts of China is still at a stage of being large but not strong, and the enhancement of its carbon sink function is constrained by factors such as tree species composition, age distribution, and management levels (corresponding to the analysis in Table 10). Improving forest quality is a long-term process, and in the short term, its industrial development may even generate new carbon emission sources.

The impact of urbanization on forest quality presents a complex nonlinear relationship (corresponding to Table 10 and Table 11). The EKC model indicates a U-shaped relationship, meaning that in the early stages of urbanization, population agglomeration, land expansion, and industrial development create a crowding-out effect on forestry space and resources. Only after urbanization crosses a certain threshold do technological progress, enhanced environmental awareness, and capital feedback begin to exert a “promoting effect” on forest quality [81]. The decomposition via the STIRPAT model further confirms that traditional urbanization elements remain the primary factors currently influencing forest quality, and their impacts are predominantly negative. This implies that, at the current stage, a certain tension of resource competition exists between urbanization and forestry development [82].

Finally, although the coupling coordination degree between new-type urbanization and forest quality is generally high (with a mean exceeding 0.9) (corresponding to Table 12), this high coupling has not directly translated into lower carbon emissions. This precisely indicates that the key constraint to synergistic emission reduction lies not in the coordination between the two systems, but in the intrinsic developmental bottlenecks of each system individually. Currently, both the green transformation of new-type urbanization and the quality improvement of forestry in China are works in progress, and their synergistic potential has not been fully realized. While fundamentally different from the ecological encroachment issues observed in rapidly urbanizing countries like India and Brazil, this situation similarly underscores the importance of coordinating urban expansion with ecological conservation.

In summary, the conclusions from the third part consistently demonstrate that achieving carbon emission reduction targets cannot be approached by considering urbanization or forestry in isolation. Instead, it necessitates systemic and differentiated intervention strategies based on their interaction mechanisms and regional heterogeneity.

4.2. Policy Recommendations

Based on the above findings, this study proposes the following policy recommendations aimed at achieving synergistic emission reduction by optimizing urbanization patterns and enhancing forest quality.

Implementing regionally differentiated urbanization and ecological coordination strategies: In response to the regional heterogeneity revealed in Table 7, policies should be precisely targeted. In the eastern and southern inverted U-shaped regions, the focus should be on consolidating the declining trend after the inflection point by deepening green industrial transformation, developing the digital economy and service sectors to further unlock emission reduction potential. In the northern and western U-shaped regions, the immediate priority is to prevent carbon emissions from rising linearly with urbanization. This requires setting stricter environmental access standards, controlling the expansion of energy-intensive industries, and increasing investment in clean energy and green infrastructure to help these regions cross the environmental inflection point sooner.

Promoting a fundamental shift in forestry from quantity increase to quality enhancement: Given the limited direct contribution of forest quality to emission reduction (Table 8 and Table 9), the future core of forestry policy should shift from area expansion to quality improvement. Specific measures include: Strictly protecting primary forest lands during urbanization to curb the disorderly encroachment of construction land, based on the transmission mechanism shown in Table 10; Promoting close-to-nature forestry and scientific silvicultural practices, such as moderate thinning, to optimize stand structure and enhance carbon storage and growth per unit area; Incorporating urban forest system development into territorial spatial planning to increase urban greenery, directly leveraging the carbon sink function of urban ecological spaces.

Guiding urbanization and forestry development towards positive interaction: To reverse the crowding-out effect of forestry in the early stages of urbanization (Table 10 and Table 11), proactive collaborative mechanisms should be designed. In urban-rural planning, explore the coordinated demarcation of ecological conservation redlines and urban development boundaries to safeguard the spatial baseline for forestry development. Utilize the technological, financial, and human capital advantages brought by urbanization to feedback into forestry modernization, for example, by promoting smart forestry and mechanisms for realizing the value of ecological products. Strengthen the green orientation of traditional urbanization factors (population, industry), promote the integration of industrial upgrading with forestry-based eco-tourism and under-forest economy, transforming competition into complementarity.

Advancing energy structure transition and systemic emission reduction in tandem: Forest carbon sinks are only one means of offsetting emissions; the fundamental solution lies in reducing emission sources. The research conclusions support a two-pronged approach: On one hand, resolutely optimize the energy structure by reducing the proportion of coal consumption and vigorously developing clean energy such as natural gas, wind, and solar power to lower carbon emission intensity at the source. On the other hand, continuously improve the quality of new-type urbanization through measures like compact city development, green buildings, and intelligent transportation to reduce the overall carbon footprint of socioeconomic activities. This will form an effective synergy of emission reduction and sink enhancement with forestry carbon sinks.

5. Conclusions

This study achieved its research objectives by examining the impact mechanisms of new-type urbanization and forestry quality on carbon emissions, and exploring new pathways for carbon neutrality during the urbanization process through analyzing the urbanization carbon emission EKC, the transmission mechanisms of forestry quality, and the regional heterogeneity of carbon emission mechanisms.

The study found that forestry quality has not effectively achieved carbon emission reduction effects, which is attributed to the overall level of forestry quality development requiring improvement. The transmission mechanism of urbanization on forestry quality is mainly manifested in traditional factors such as population urbanization and industrial structure. The carbon emission reduction effect of the symbiotic coupling between urbanization and forestry is insufficient, and enhancing the levels of new-type urbanization and forestry quality represents a new pathway for carbon neutrality.

The highlights of this study lie in: (1) An evaluation system for new-type urbanization and forestry quality was made to calculate the urbanization level and forestry quality. (2) Environmental Kuznets Curve models were made to understand the relationship between urbanization, forestry quality and carbon emissions. (3) The STIRPAT model was used to find specific ways in which forestry quality affects carbon emissions. This study examines the carbon emission mechanism of new urbanization under forestry quality regulation through the extended STIRPAT and EKC models, enriching the content and perspectives of urbanization, sustainable forestry, and carbon reduction research.

This article compares the main conclusions of the study with existing research, focusing on the coordinated development of urbanization and forestry, environmental curves, and sustainable development. The limitations of this study lie in the limited sample size and insufficient comprehensiveness of the indicator data. Meanwhile, the regional differences in China’s forestry resources are significant, and existing literature has also analyzed the transmission mechanisms and symbiotic coupling between urbanization and forestry from different regional perspectives. Due to space limitations, this study did not expand on these discussions. Future research will address these deficiencies. Another limitation of this manuscript is the lack of consideration for inter-regional carbon emission flows and externalities. The reason for this limitation is that: (1) existing mainstream carbon emission impact models, such as EKC and STIRPAT models, have relatively little exploration of the flow factors of carbon emissions. Based on the requirements of the research model, this manuscript did not conduct in-depth research on carbon flow. A common approach in existing research is to add carbon emission impact indicators for adjacent regions in EKC and STIRPAT models, which increases the model’s degrees of freedom and reduces the effectiveness of statistical regression when the sample size is difficult to increase. (2) The carbon emission data used in this manuscript is based on the conversion of heterogeneous energy consumption data in the process of regional economic and social development. The measurement of carbon emissions is based on the productive characteristics within the region, rather than the issue of carbon mobility. In future studies, the impact of carbon emissions on adjacent regions will be introduced into the research scope by increasing the sample size.