A Comprehensive Model Assessment of China’s Forestry and Climate Change

Abstract

The relationship between maximizing forest revenue and reducing environmental pollution has been a challenging one. It is every country’s responsibility to protect its forest reserves and mitigate climate change. Studies on the relationship between forest economic models and climate change are limited, and most of them focus on maximizing forestry products. This study aims at filling the gaps and makes scientific contributions by providing a detailed account of various economic models and their correlations with climate change, as well as identifying the ecological footprint of forest products, fossil fuel consumption, forest cover, foreign direct investment, economic growth, and population in terms of carbon dioxide (CO₂) emissions. In this study, we observed that most forest economic models focus on forest profit maximization and disregard climate impact. The empirical results suggest that the ecological footprint of forest products increases CO₂ emissions. In addition, forest cover helps to reduce CO₂ emissions. A case study of China’s tremendous growth and the associated CO₂ emissions levels reported a recent decrease in such levels, largely due to an increase in forest cover. Although these findings are not exhaustive, they provide new insights into forestry economic models and the impact of climate change, offering theoretical and practical implications for future reference and forest governance.

1. Introduction

Forests offer a variety of goods and services, including marketable goods such as timber and non-marketable qualities, such as aesthetic value, carbon sequestration, watershed protection, biodiversity preservation, and climate regulation [1]. These products and services are produced as a result of intricate biological and physical interactions between the environment, developing stock, and management practices over extremely lengthy management periods. Thus, several nations have been compelled to formulate plans for efficient forest management as a result of contemporary environmental issues, including greenhouse gas emissions (GHG). However, the coexistence of industrialization and forest growth faces challenges due to human population growth, sustenance, and development; industrialization has progressed at the expense of forest reserves. Forest management is a subfield of forestry that focuses on silviculture, protection, and forest regulation as well as general administrative, legal, economic, and social issues. This covers timber management, aesthetics, recreation, urban values, water, wildlife, inland and nearshore fisheries, wood products, plant genetic resources, and other values associated with forest resources.

Recently, in light of these challenges, forest economists have formulated various models and interventions that consider possible measures to balance forest output, climate change, and human impact on forests. On the other hand, the traditional forest management models emphasize mainly the short- and long-term profits to continually generate income while ignoring climate consequences. Most developed countries, including China and the United States, which previously boasted rich forest reserves, are now at the mercy of climate change challenges, as they keep recording high values of GHG emissions due to industrialization. Interestingly, these forest reserves have the potential of suppressing GHG emissions amidst industrialization. Approximately 4 billion hectares (ha) or 30% of the Earth’s surface is covered by forests worldwide [2], though there has been a decrease in the planet’s forested area in the past decade. In China, it has been estimated that forests make up approximately 22% of the country’s territory or 206 million hectares; of this total, 5% are classified as primary forests, which are the biodiverse and carbon-dense forest types [3]. Nonetheless, the amount of forest cover has changed over the past decade, with estimates of a 30.02% net gain, or roughly 20,120,970 hectares (ha) of land [3,4,5]; however, this enlargement has not been sufficient to compensate for GHG emissions.

Research on climate change effects in China has primarily concentrated on how industrialization has accelerated climate change, with little attention paid to how climate change has affected China’s forestry industry [6]. Previous research has shown the impact of climate change in China, taking into consideration weather pattern changes in the past century and the annual mean surface air temperature (SAT) increase (which has been recorded with an increased range of 0.03 °C (10 year)⁻¹ to 0.12 °C (10 year)⁻¹ [7]. This warming has been particularly noticeable in Northern China during the winter and spring seasons. In addition, sand/dust storm frequencies have increased, tropical cyclone landfall frequencies have marginally reduced, and proxy data indicate that the frequency of droughts and floods in eastern China has increased. The main cause of the observed surface warming was likewise identified to be GHG concentrations in the atmosphere. It is of note that the increased aerosol concentrations and cloud cover observed in the summer in the Yangtze River and Huaihe River basins are related to the cooling trend [7,8].

Models have been long used in forestry management and economics to plan and schedule regeneration, management actions, and harvests, as well as to forecast growth and production [9]. Additionally, these models are employed to value both marketable and non-marketable forest products and optimize management choices for any forestry land, including rotation period, thinning, and pruning regimes. Nonetheless, forest costs have skyrocketed, and forest benefits have decreased due to the competitiveness of some products and services, changes in land ownership and management aims, and differences in various afforestation methods and technologies used in forest lands. The discipline of forest economics has expanded in terms of the production, distribution, and consumption of forest goods and services in China. The neo-classical economic framework and sustained timber management systems are the foundation of the existing forest economic models [10,11]. Thus, economic models are utilized to understand forest land uses and conduct an economic and financial analysis of forestry investments.

In this study, we utilized forest economic models used in forest management as well as other econometric models and tools to assess the relationship between climate change and forestry in China. In addition, an empirical analysis was conducted to achieve reliable and robust results. In our quest to achieve this goal, the following specific objectives were addressed. (1) Review Faustmann’s basic economic model, identify gaps, and suggest the way forward in the sustainability space. (2) Establish the relationship between the ecological footprints of forest products, fossil fuel consumption, land area, foreign direct investment, economic growth, and pollution in terms of carbon dioxide (CO₂) emissions, using China as a case study. To achieve these objectives, theoretical and practical contributions are made and gaps are closed. First, the study considers the literature focusing on the Faustmann basic economics model. Various studies have built models using this framework; however, there are gaps to fill concerning forest management and environmental sustainability. Second, the study utilizes the ecological footprints of forest products in terms of CO₂ emissions. This is a forestry variable that is underutilized in the literature. Its impact on CO₂ emissions is important to guide researchers and countries in protecting their forest reserves. Again, the literature reviewed and the variables used in this study will provide a strong foundation for future forestry economics–based research. Finally, the findings, recommendations, and policy implications suggested in this study will assist future studies and policymakers in enhancing forestry management and research.

2. Literature Review on Forest Economics Models and Climate Change

2.1. Climate Change and China’s Forestry

Climate change has been predominantly caused by industrialization since the 1800s, along with increased human activity, primarily as a result of the burning of fossil fuels, deforestation, GHG emissions, etc. [12]. Due to the long-term changes in weather temperatures and patterns caused by these operations, notwithstanding their necessity, the environment has already been significantly impacted, as seen in the shrinkage of glaciers and ice sheets, changes in the geographic distribution of plants and animals, and variations in the growth and development of plants [13,14]. Due to its large population and significant GHG emissions, China is particularly susceptible to the effects of climate change [15]. China’s economy has grown rapidly in recent years. However, with only 7% of the world’s land available as arid land to supply food for 22% of the world’s total population [16], the effects of climate change pose a significant threat to forestry and water supplies.

Climate change has been identified as a key driver of the dynamics of terrestrial ecosystems due to its impact on the development and distribution of vegetation [17]. Nonetheless, the forest is the most complex terrestrial ecosystem [18], contributing significantly to the carbon cycle and responsible for 49% of terrestrial gross primary production in China [19]. Approximately half of the carbon emitted from fossil fuels globally is absorbed by forests [20,21]. With a carbon stock of over 84.27 billion tons and a land surface area of approximately 22% (2009–2013), China’s forests are the world’s largest wooded area and a substantial part of the nation’s terrestrial ecosystems. Nonetheless, China is the primary producer of carbon dioxide in the world, accounting for around 28% of all emissions [21]. Over the past ten years, the country’s gross domestic product (GDP) has grown 7% on average annually, significantly accelerating emissions. China has produced more greenhouse gas emissions annually than any other nation in the world over the previous 10 years, including carbon dioxide, methane, and nitrous oxide [22,23] (Figure 1).

China has a vast forest cover; however, it is unclear how much of its emissions are absorbed by its forests [21]. Due to decades of rapid industrialization, China is currently experiencing an environmental crisis that not only endangers the health and way of life of its citizens but also the worldwide effort to combat climate change [22]. Additional environmental difficulties have been brought on by its carbon-intensive industry [6]. Due to this issue, the nation runs the possibility of suffering from severe climate change effects in the future, such as flooding and droughts [22]. Despite these difficulties and being one of the biggest importers, exporters, and consumers of wood and wood products globally, the nation continues to work to reduce pollution [21]. Since 1998, the nation has implemented several stringent domestic forest preservation policies, including a 2016 ban on all commercial logging in its natural forests [24]. These measures have the potential of reducing the country’s GHG emissions. It is clear that China has expanded its forested areas. To combat desertification and soil loss, develop thriving lumber and paper industries, and combat climate change, billions of trees have been planted in recent decades [24].

China has effectively increased its forest area and stocking volume in recent years as a result of significant increases in the establishment of timber plantations and shelterbelts, as well as different strategies to reduce timber harvest and wood consumption [1]. Such policies can help to improve China’s forest resources. The Chinese forest issue has been exacerbated by many factors, but ineffective and inappropriate practices are to blame for the majority of the problems faced in the past [18]. It is envisaged that multiple interconnected forestry types will aid in the nation’s recovery through more effective management in the coming decades. Among these remedies are a robust commercial forestry industry with intensive plantations and an integrated processing industry, a well-established environmental forestry industry, and a sizable multiple-use forestry industry that takes into account the objective of sustainable forest development and effective government forest policy.

2.2. Overview of Forest Economics Models

To examine the economic features of forests, numerous models have been created. With the use of these models, it is possible to assess the advantages and disadvantages of various land-use alternatives, determine the value of forests as carbon sinks, and examine the effects of policy and regulatory changes on the forestry industry. Typical forms of forest economic models include forest management models, which optimize tree planting and harvesting to maximize long-term financial benefits; carbon sequestration models, which consider the carbon stored in trees and its potential economic worth; and timber supply models, which are used to predict the availability and cost of the material in the future, taking into consideration elements such as the rate of forest growth, the demand for wood products, and the effects of legislative and governmental changes. To determine the most economically efficient land use mix, forest land use models are used to examine the trade-offs between various land use possibilities, including forestry, agriculture, and urban development. Models of recreational demand are used to determine the demand for outdoor pursuits such as hiking, camping, and birding as well as to determine the financial worth of these opportunities. Due to the generalization issues of these models, scholars have differentiated them into specific models used in various studies on forestry management to achieve specific objectives. Some of these include the Faustmann model, sometimes referred to as the basic economic model, which is used to determine the best time to harvest and replant a forest stand to maximize the present value of the net income the stand would produce throughout its lifetime. The gravity model is used to measure the forest product trade flow between two countries. The stumpage price model is used to estimate the price that a landowner can expect to receive for the timber. The forest growth yield model is used to forecast the development and output of a forest stand through time, accounting for elements including species, site quality, and management techniques. The forest sector economic model is used to examine the financial performance of the industry, including the production and consumption of forest products, the trade of those items, and the financial effects of forest management strategies, among other things. Similarly, to achieve the objectives of this study, we focus on the basic economic model of forest management developed by Faustmann. This model has served as a basic model guiding recent studies and models.

2.3. The Faustmann Model (Basic Economic Model)

To optimize the value of the net revenue produced by the forest stand over its lifetime, the Faustmann model predicts the optimal rotation age of a forest stand, which is the age at which the stand should be harvested and replanted. Martin Faustmann, a German forester, created the model in 1849. It is based on the time value of money theory, which holds that a given sum of money loses value over time as a result of the opportunity cost of not being able to invest it. The Faustmann model’s ideal rotation age is the age at which the stand’s present value of net revenue is at its highest. The cost of creating a new stand, the rate at which the stand develops and yields wood, the rate at which the price of timber rises over time, and the rate at which the costs of harvesting and replanting the stand rise over time are all taken into account by the model. The Faustmann model has been widely applied in forestry and has significantly influenced how forests are managed.

Land and time are the two main production elements in the forestry industry. Land can be used for many activities, including forestry, development, and agriculture. The capacity of a given piece of land to provide the greatest amount of profit for its owner determines its optimal use. However, a certain piece of land’s physical and chemical properties can restrict its potential applications. Once the viable activities on a certain piece of land are recognized, economics can be used to determine the most profitable use of land and the best management plan to achieve particular goals. The maximization of the present value in terms of competitive markets determines how land will be used. If a piece of land is up for sale, prospective buyers will compute the net present value (NPV) of that use by taking into account the predicted cash flow connected with that use. They will make bids for the land based on these calculations, and the highest bidder will ultimately buy it [1]. The compounded net cash flow of land use is given by the formula in Equation (1).

$$\mathrm{V}=\frac{{\mathrm{R}}^{\mathrm{t}}}{{(1+\mathrm{i})}^{\mathrm{t}}-1}$$

(1)

where R^t represents all variable revenue compounded to year t, and net represents all variable costs compounded to year t. The willingness to pay for bare land (WPL) per unit area is determined by the equation.

The land expectation value (LEV) or land rent, which denotes the highest price an investor might pay for a plot of land while still receiving a minimum acceptable rate of return (MAR) acceptable to them is calculated using this fundamental concept in forest economics. The formula utilized in the literature on forest economics is discussed by Navarro [25], and his research demonstrates that at least three different variants are used for economic modeling in forestry. Again, the value of the Faustmann approach for forestry and general economics is evaluated broadly in Samuelson [26]. The following four fundamental presumptions must be true for the Faustmann formula to be used correctly. First, the capital market is ideal, allowing investors to borrow or lend any amount at the current interest rate, which is fixed for all subsequent periods and known to investors. Second, future wood prices—at which all products are sold and all expenditures are incurred—are predictable and consistent over all periods. Third, future timber yields are predictable and stable. Fourth, in an ideal market, forest land can be acquired, rented out, or sold.

The Faustmann method does not analyze land improvements or raising livestock since it assumes that land is the only factor that determines production. In addition to its natural soil properties, the property also brings in money for its owner through improving the soil and raising livestock [25]. These components reflect a cost that needs to be factored into the land’s value. When considering converting land to forestry, investors who want to maximize the capitalized value of their land investment should be prepared to pay as much as possible for raising livestock and land improvements because changing land use is not free. The cost of site preparations, infrastructure like roads, and stock-growing through afforestation are included in the current value of the land use conversion capital. The forest expectation value (FEV) is produced by combining the capital value of land use conversion with the land’s value. The König formula shown in Equation (2) can be used to calculate the FEV and treats the initial growing stock and necessary infrastructure as a part of the capital of the land assets.

$${\mathrm{V}}_0=\frac{{\mathrm{R}}_{\mathrm{H}}-{\mathrm{C}}_{\mathrm{a}}}{{(1+\mathrm{i})}^{\mathrm{t}}-1}-{\mathrm{C}}_{\mathrm{f}\mathrm{c}}$$

(2)

where Ca is the cost of afforestation, Cfc is the cost of converting land use, V₀ is the FLV, and RH is the revenue from timber, net of harvesting expenses, compounded to year t.

When deciding whether or not to permanently convert cropland into forestry, FLV varies from LEV in that it considers land development and increasing stock as fixed capital in addition to the land. LEV does not include these elements as part of the investment capital because it sees land as the sole agent of production. LEV maximizes the value of the bare ground that can be used for a forest, whereas FLV maximizes the value of the products from the forest. There are many different applications for the Faustmann formula, and it recently seen some extensions. Limitations of the compensation technique for nature conservation were investigated by Hampicke [27]. The Faustmann model was used to determine the amount of compensation needed to store carbon on private forest land by Huang et al. [28]. Manley et al. [29] used this approach in New Zealand on pine plantations, and [30] reported on transitional nations in Eastern Europe. Utilizing the Faustmann formula, research was conducted on silvicultural investment in the presence of policy or regulatory uncertainty [31]. It was also utilized to analyze European beech in Northwestern France under storm risk and price uncertainty. For a long period, the theory of forest economics was dominated by the struggle between the land rent theory, which is largely based on Faustmann’s work, and the Faustmann theory.

The current methods of managing forests were investigated in research by [32], which placed special emphasis on determining when, where, and how much of the forest may be harvested to produce wood while taking into account a variety of operational and conservation issues. The “triad zoning” strategy, which suggests separating a managed forest into the three zones of (a) conservation, (b) wood extensive production, and (c) wood intensive production serves as the foundation for the multi-objective optimization model employed in this work. Additionally, in an attempt to establish the potential impact of climate change on forestry products, [33] merged the Woodstock model with the computable general equilibrium model, and the findings of the investigation revealed very significant detrimental effects on forest products. The study went on to suggest that adjusting to climate-related changes by replacing failing softwood plantations with drought-resistant softwood seedlings or hardwood seedlings can minimize these negative effects and, in the latter instance, favorably improve the supply and output of hardwood timber. The study takes into account climate change impacts on the forest; however, it focuses only on solutions that maximize outputs and profitability and ignores other climate change issues with regard to the economy.

Rinaldi et al. [34] introduced a theoretical framework for robust optimization of forest management under uncertainty, based on control theory. They contend in their study that forest owners view their decision support system as an approximation of a real, unidentified model. They contend that worries about model misspecification motivate them to look for a single harvesting rule that performs well across a collection of models that are statistically comparable to their approximation. They use a stylized forest model to explore the effects of uncertainty on harvesting decisions and also consider the role of information aimed at reducing or making forest owners aware of such uncertainty. They believe that accounting for mistrust of decision support systems in modeling harvesting behavior is particularly relevant given the uncertainty induced by climate change. The findings showed that model uncertainty has an impact on harvesting rate and, consequently, forest growth. To emphasize stand volume over harvest income, forest managers who are worried about model uncertainty reduce harvest levels. Furthermore, the dissemination of information influences perceived levels of uncertainty, which impacts harvesting practices and forest development. This reinforces the significance of information as a tool for implementing forest policy.

2.4. Literature Summary and Study Contributions

From the literature reviewed, it is surprising to note that forest economics models used in the literature are very limited. In addition, the available models focus on forest products and profit maximization while neglecting environmental sustainability components. Moreover, studies that include empirical analysis of forestry management and climate change variables are limited, creating a wider gap between forestry management and environmental sustainability. As a result, the novelty of the study is to introduce environmental sustainability variables for empirical analysis of forest economics models and environmental sustainability. It will make scientific and practical contributions by using developed econometric models and tools to assess the relationship between climate change and forestry in China, taking into consideration environmental sustainability variables. China leads the world as the highest CO₂ emitter. The country also has a large forest cover. However, research on forestry and climate change is limited; this study aims to fill that gap. Additionally, it discusses the relevant and current literature in order to expose the scientific and practical problems and gaps in the forestry industry. It also provides a theoretical foundation for future forestry and climate change studies. Finally, policy suggestions made from the findings will help in formulating policies that will assist in forest governance.

3. Data and Methodology

3.1. Data Collection and Variables Used

Data were collected using two methods (qualitative and quantitative). The first part of the study involved searching databases for the relevant literature to fulfill the objectives of this study. The qualitative technique used forest economics models emphasizing the Faustmann basic economic model and forest management, and the quantitative technique and analysis included building an econometric model that assesses the influence of selected variables and CO₂ emissions (Figure 2).

To summarize the body of knowledge already available on forest economics models, studies have utilized the Systematic Literature Review (SLR) methodology [35,36,37]. The SLR approach involves selecting, evaluating, and analyzing retrieved articles to identify, and assess the current literature. The steps used in sourcing and analyzing the literature are as follows.

The first step was to gather the publications from different databases, including ScienceDirect, Google Scholar, ResearchGate, Scopus, CAB Abstracts, and Forest Science. The search for more in-depth articles for the particular subsections of the study resulted in successively improved articles. A total of 200 articles were scanned, and the ones that stood out as being the most pertinent to the subject were chosen. Unpublished papers from conferences and congresses were filtered out; only published articles were used for the purposes of traceability. We began with broad search terms. The search style and keywords used in retrieving relevant articles were as follows: Concept 1: “forest economics models” OR “forest management” OR “Forestry” OR “Forest products”; Concept 2: “Climate change” OR “Environmental sustainability” OR “Sustainable”; Concept 3 “China” OR China’s Forestry” OR “Chinese Forestry”. These concepts were combined with the following key combinations: #1 and #2 and #3 with the period limited to the last three decades (1990 to 2022).

The papers that were chosen were further screened using the inclusion and exclusion method. Articles that discuss peer-reviewed forest economics models and climate change were included. Articles that did not mention forest economics models were disqualified. To achieve the major goals of this research, only articles that addressed forest economics models for forest management and climate change in China were analyzed. The articles reviewed were focused on China according to the objectives of the study. For the qualitative data, the period selected was 1990–2022. It is worth noting that, due to this robust strategy, the number of articles for this review was surprisingly lower than what we anticipated.

3.2. Quantitative Data Collection and Variables

The study solicited data for China from 1990 to 2018. This timeframe, which was different from that used for the qualitative data, was due to data unavailability. Variables were selected in relevance to forest economics, forest management, and climate change measurements. The analysis was centered on an endogenous model that included carbon dioxide (CO₂) emissions, ecological footprints for forest products, fossil fuel consumption, land area, foreign direct investment, economic growth, and population.

Table 1. Variables used and data source.

Abbreviation	Name of Variable	Unit of Measurement	Data Source
CO2	Carbon dioxide emissions	Kilotonnes	FAO, 2022
EFF	Ecological footprint of forest products	Ecological footprint (gha)	Global Footprint Network, 2022
FFC	Fossil fuel consumption	Fossil fuel consumption (% of total final consumption)	World Bank, 2022
LA	Land area	Forest area (% of land area)	World Bank, 2022
FDI	Foreign direct investment	Foreign direct investment, net inflows (% of EG)	World Bank, 2022
GDP	Economic growth	EG (current US$)	World Bank, 2022
POP	Population	Total population	World Bank, 2022

displays the data used, variable explanation, measurement units, and source.

3.3. Empirical Model

The ordinary least square (OLS) estimation method was employed to run the multiple linear regression for the study. In a linear regression model, the OLS regression optimization technique aids in locating a straight line that is as near to the data points as is practical. Due to its simplicity, effectiveness, and adaptability, this method was chosen, since it is a prevalent and extensively used method for statistical modeling [38]. To choose the best model, the study used two basic strategies. First, the study’s primary method for selecting the model was a stepwise variable selection based on the Akaike information criterion (AIC). The comprehensive model intended for the study served as the foundation, and variables were gradually added and removed. As a result, the final model was larger and included all forward selection factors. In addition, the correlation among the variables was checked, and the results revealed a very high correlation. Hence the collinearity test was conducted to aid in variable selection, which led to a drop in some variables. Finally, we arrived at the current model used for this study.

Based on this multiple linear regression model using carbon dioxide (CO₂) emissions, ecological footprints for forest products, fossil fuel consumption, land area, foreign direct investment, economic growth, and population, the model was developed. Thus, the econometric model/equation is formulated below, in Equation (3).

CO_2it = α + EFF_it^β1 + FFC_it^β2+ LA_it^β3 + FDI_it^β4 + GDP_it^β5 + POP_it^β6

(3)

Due to the respective structure of the individual datum, the data are non-linear and need to be log-transformed to their natural logarithms for uniformity, as expressed in Equation (4).

log_e CO₂ = α + ∑αlog_e (X_kth)

(4)

where CO₂ is carbon dioxide emissions, X_kth represents the explanatory variables, and β₀ represents the model coefficients. Therefore, the model can also be written as Equation (5):

log_e (CO₂) = α + ∑αlog_e(EFF, FFC, LA, FDI, GDP, POP)

(5)

To test the long-run association between variables as expressed in Equation (1), variables’ values are transformed in their natural logarithm, as expressed in Equation (4). When using statistical models, transformed data are simpler to work with because they are significantly less skewed and typically have fewer severe outliers. Therefore, the log-transform of the model as expressed in Equation (5) can also be expressed in a functional model, as shown in Equation (6):

InCO_2it = α + β₁lnEFF_it + β₂lnFFC_it + β₃lnLA _it + β₄lnFDI _it + β₅lnGDP_it +β₆lnPOP_it + u_it

(6)

where CO₂ is Carbon dioxide emissions, EFF is the ecological footprint for forest products, FFC is fossil fuel consumption, LA is land area, FDI is foreign direct investment, GDP is gross domestic product, and POP represents the population. (β₁–β₆) are the parameter estimates, α is the intercept, t is the period, i refers to the ith population, and u represents the error term. The respective coefficients in Equation (6) are expressed in their standard form. However, the expected sign conditions are β₁(−), β₂(+), β₃(−), β₄(+), β₅(−), and β₆(−).

3.4. Unit Root Test

A non-stationary time series has trend and seasonality components that will affect the forecasting of the time series. As a result, time series data must be stationary so that previously unnoticed components can be identified to strengthen their forecasting. The Augmented Dickey–Fuller Test is used to test for the stationarity of the series. The Augmented Dickey–Fuller test allows for higher-order autoregressive processes by including Δyt − p in the model. Equation (7) is thus illustrated below:

Δyt = α + βt + γyt− + δ2Δyt − 2

(7)

3.5. Correlation Test

The Pearson correlation coefficient is a measure of linear correlation between two sets of data. It is a statistical test that estimates the strength between the different variables and their relationships. It is independent of the variables’ units of measurement. In that case, the differences in the units of measurement of variables do not influence the coefficients. The correlation coefficient between the variables is symmetric, which means that the value of the correlation coefficient between Y and X or X and Y will remain the same. The study used the Pearson correlation test to determine the strength of correlation among the variables. It is calculated using Equation (8) below.

$$r=\frac{\sum {(x}_i-\mathsf{ẋ}\left)\right(y_i-\mathsf{ẏ})}{\sqrt{\sum {(x_i-\mathsf{ẋ})}^2\sum {(y_i-\mathsf{ẏ})}^2}}$$

(8)

where r is the correlation coefficient, x_i is the value of the x-variable in a sample, ẋ is the mean of the values of the x-variable, y_i is the value of the y-variable, and ẏ is the mean of the values of the y-variable.

3.6. Collinearity Test

Collinearity implies two variables are near-perfect linear combinations of one another. Multicollinearity involves more than two variables. In the presence of multicollinearity, regression estimates are unstable and have high standard errors. Variance inflation factors measure the inflation in the variances of the parameter estimates due to collinearities that exist among the predictors. This is a measure of how much the variance of the estimated regression coefficient β_k is “inflated” by the existence of correlation among the predictor variables in the model. A VIF of 1 means that there is no correlation between the kth predictor and the remaining predictor variables, and hence the variance of β_k is not inflated. The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity and require correction. VIF is calculated using Equation (9) below.

$$\mathrm{V}\mathrm{I}\mathrm{F}=\frac{1}{1-{\mathrm{R}}_{\mathrm{k}}^2}$$

(9)

4. Results and Discussion

4.1. Trends of the Variables

China’s CO₂ emissions have seen a tremendous increase in the last two decades (see Figure 3A). China has consistently been the most polluting country in the world. In terms of the ecological footprints of forest products, the country recorded a stable trend in the late 1990s and early 2000s. Since 2008, the country’s footprint in terms of forest products has increased massively, and it continues to increase, as shown in Figure 3B. Fossil fuel consumption in the country is still on the rise, as seen in Figure 3C. The country depends on fossil fuels to support its energy usage. China’s land area is increasing, as can be seen in Figure 3D. Direct investments from foreign countries have seen a decrease in the last decade, as shown in Figure 3E. China has invested heavily in other countries, such as those in Africa; this may related to a reduction in the investment allowed such countries. The country’s growth in terms of GDP has been very impressive and has gained recognition around the globe. This growth was seen mostly in the last two decades (see Figure 3F). Since the 1990s, China has consistently been increasing its total land area, as can be seen in the trend in Figure 3G. The country’s depopulation policy is still in progress; the population continues to increase but at a decreasing rate, beginning in the early 2000s. This can be seen in Figure 3G.

4.2. Descriptive Statistics

Descriptive statistics were determined for the study. The mean, median, standard deviation, maximum and minimum, skewness, kurtosis, and Jarque-Bera data were determined. Throughout the years under study, CO₂ emissions in China saw a maximum of 16.14, with the lowest value of 14.59 and a mean value of 15.43. Ecological footprints of forest products saw a mean value of 19.24, a maximum of 19.61, and a minimum of 19.06. With regard to fossil fuel consumption, the value peaked at 4.53, with a low of 4.32 and a mean value of 4.42. Other figures are presented in

Table 2. Descriptive statistics.

	CO2	EFF	FFC	LA	FDI	GDP	POP
Mean	15.4400	19.2401	4.4250	2.9811	1.1395	28.4677	20.9730
Median	15.4490	19.1618	4.4403	2.9848	1.2565	28.3016	20.9826
Maximum	16.1490	19.6051	4.5301	3.1330	1.8224	30.2625	21.0617
Minimum	14.5918	19.0590	4.3152	2.8138	−0.0343	26.6118	20.8501
Std. Dev.	0.5509	0.1612	0.0674	0.0994	0.4769	1.1980	0.0612
Skewness	−0.0244	0.7543	−0.1072	−0.0927	−0.9491	0.0306	−0.4081
Kurtosis	1.4157	2.2079	1.6264	1.7311	3.1431	1.6565	2.1152
Obs.	29	29	29	29	29	29	29
Jarque-Bera	3.0359	3.5084	2.3353	1.9872	4.3785	2.1857	1.7511

.

4.3. Unit Root Test Results

The Augmented Dickey–Fuller Test for stationarity was used. All variables used for the study were tested for stationarity. The unit root was tested at both constant and trend levels. To ensure stationarity of the variables, the test was taken through three levels: level, first difference, and second difference. At the level, all variables were non-stationary. At the first difference, some of the variables were stationary. Finally, all variables became stationary at the second difference at a 1% level. This confirmed the stationarity of the variables before regression was done. As a result, regression for variables based on these values was obtained.

Table 3. Unit root test results.

Variables	Level		1st Difference		2nd Difference
	C	T&C	C	T&C	C	T&C
CO2	0.7526	0.7095	0.9880	0.2529	0.0000 ***	0.0000 ***
EFF	0.9983	0.8430	0.0331 **	0.0656 *	0.0000 ***	0.0000 ***
FDI	0.1100	0.0578	0.0189 **	0.0381	0.0015 ***	0.0065 ***
FFC	0.9344	0.5169	0.0007 ***	0.0046 ***	0.0000 ***	0.0000 ***
GDP	0.7927	0.4243	0.0514 **	0.1627	0.0008 ***	0.0033 ***
LA	0.4223	0.9926	0.8482	0.7531	0.0001 ***	0.0005 ***
POP	0.5080	0.6579	0.0487 **	0.6323	0.0001 ***	0.0022 ***

Note: *** is 1%, ** is 5%, * is 10%. Null is rejected at 5%. C is Constant; T&C is trend and constant.

displays the unit root test results.

4.4. Correlation Test Results

A test on the correlation between variables was conducted to determine whether a statistically significant correlation was present between CO₂ emissions and the independent variables used. The findings indicate that EFF and CO₂ have a strong positive correlation at a significance level of 0.0000. FDI has a negative but insignificant correlation with CO₂. FFC, GDP, LA, and POP have a positive correlation with CO₂ emissions at a 1% level. These and other correlations among variables can be seen in

Table 4. Correlation results.

Variables	CO2	EFF	FFC	LA	FDI	GDP	POP
CO2	1.0000
EFF	0.5821 ***	1.0000
FFC	0.6827 ***	0.6634 ***	1.0000
LA	0.4857 ***	0.1058 ***	0.3857 ***	1.0000
FDI	−0.1878	−0.3981 **	−0.1965	−0.2112	1.0000
GDP	0.6886 ***	0.4234 ***	0.5769 ***	0.4948 ***	−0.2313	1.0000
POP	0.3625 ***	0.4717 ***	0.2730 ***	0.4907 ***	−0.1290	0.1783 ***	1.0000

Note: *** 1%, ** 5% Null rejected at 5%.

.

4.5. Colinearity Test Results

The collinearity test was conducted to determine the multicollinearity problem among the independent variables. The test results in

Table 5. VIF test results.

Variable	VIF
EFF	4.7686
FFC	6.0905
FDI	2.7431
GDP	8.3735
LA	4.3313
POP	8.2659

indicate the non-existence of multicollinearity among the variables. An initial test was conducted due to close-to-perfection figures in the correlation test. The initial test had multicollinearity issues; however, after dropping and changing variable forms, these multicollinearity issues were corrected. According to the centered VIF, all figures are less than 10, which indicates that there are no signs of serious multicollinearity among the variables.

4.6. Regression Results

The ordinary least square (OLS) regression was conducted to determine the impact of the independent variables (ecological footprint of forest products, fossil fuel consumption, foreign direct investment, gross domestic product, and population) on the dependent variable (CO₂ emissions). According to the results, there is an R² of 99.7%. This shows that roughly 99% of the dependent variable was explained by the independent variables. Since this is close to 100%, it can be concluded that these variables explained CO₂ emissions very well. From the results, it was found that ecological footprints have a significant negative influence on CO₂ emissions at a 1% level. This shows the relationship between footprints in the forest sector and pollution levels. Based on the results, the Chinese forest footprint is a strong climate change indicator. The negative association means that as the forest footprint increases, the pollution levels reduce. This indicates that a 1% increase in the ecological footprint of forest products decreases CO₂ emissions by 0.58%. This finding confirms previous studies such as [39,40], which also found ecological footprints to reduce carbon emissions. The study also considers the association between FDI and pollution levels. Based on the results, we found that direct investment from foreign countries has a positive relationship with CO₂ emissions. The results give a clear indication that foreign direct investment in the country increases CO₂ emissions. These results can be interpreted as follows: whenever there is a 1% increase in foreign direct investments, there is also an increase in CO₂ emissions. Direct investment has been one of the indicators identified in past studies to cause environmental pollution. Studies such as [41,42] confirm this finding. Due to the high consumption of fossil fuels around the globe, the study aimed to investigate its relationship with pollution levels in China. On the relationship between fossil fuel consumption and CO₂, the results indicated a positive relationship between the two. Explaining it further, we can say that a 1% increment in the use of fossil fuels in the country increases pollution levels by 0.92%. Non-renewable energy has been identified in these modern times as a major contributor to climate change. The assertions made by previous studies [43,44] have been confirmed in this study. This means that fossil fuel consumption has been detrimental to the Chinese economy in terms of climate change assessment. Every year, China increases its forest cover as a means to mitigate climate change issues. This forest cover is intended to offset the high pollution in the country. As a result, the study aimed at examining its association with CO₂ emissions to determine if it has helped mitigate climate change over the years under study. Based on the results, an increase in forest cover reduces pollution levels in China. Thus, we can say that whenever the country increases its forest cover by 1%, its CO₂ emissions decrease by 8.9%. These results correlate with past studies [45,46,47,48], which found that increasing forest cover reduces pollution. Economic growth is one variable that is dominant in most pollution studies. This is because recent studies have found that the growth of economies is associated with industrialization, and industrialization increases pollution. Based on these findings, the study measured China’s GDP against its pollution levels. According to the results, the rapid growth in the Chinese economy over the past two to three decades has contributed massively to pollution levels. Specifically, we found that a 1% increment in China’s economic growth increases CO₂ emissions by 0.26%. This can be attributed to the increase in industrialization in the country. This finding is in tandem with many past studies, including [39,41,49]. Finally, the study established the relationship between China’s population and its pollution levels. The Chinese population has seen continuous reductions due to birth control policies. Concerning this, the study intended to establish if the population trend of the past three decades has influenced pollution levels. The results reveal a negative association between population and CO₂ emissions. This means that in the years under study, China’s population trend has reduced its pollution. Thus, a 1% increment in the country’s population decreases pollution levels measured in CO₂. This result confirms past studies [39] but is inconsistent with [41], who found opposite results in West Africa. These results can be seen in

Table 6. Regression results. Dependent Variable CO₂.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
EFF	−0.5809	0.1339	−4.3390	0.0003 ***
FFC	0.9235	0.6833	1.3517	0.0190 **
LA	−8.9217	1.6274	5.4821	0.0000 ***
FDI	0.1060	0.0189	5.5946	0.0000 ***
GDP	0.2637	0.0708	3.7260	0.0012 ***
POP	−10.2885	1.2183	−8.4446	0.0000 ***
C	204.0864	24.2000	8.4333	0.0000 ***
Obs.	29
R-squared	0.9971
Adjusted R-squared	0.9963

Note: *** 1%, ** 5%, Null rejected at 5%.

.

The final model can therefore be written as Equation (10) below.

CO₂ = 204.09 − 0.58EFF + 0.92FFC_it − 8.92LA_it + 0.11FDI + 0.26GDP_it − 10.29POP

(10)

5. Conclusions and Implications

5.1. Conclusions

Forests will continue to be beneficial to human survival. As the world works toward a green and sustainable environment, it becomes a responsibility for every country to protect its forest reserves to save the planet from climate change. In saving the forest, its management becomes key, as products from forests provide necessities for people. This study had two main objectives; the first was to review various forest economics models and their relationship with climate change, while the second was to establish the associations between ecological footprint of forest products, fossil fuel consumption, forest cover, foreign direct investment, economic growth, and population and CO₂ emissions. A qualitative analysis was conducted for the first objective after reviewing about 200 articles from 1990 to 2022 related to forest economics models and climate change in China. In the second objective, the study used data from 1990 to 2018 in China and adopted the ordinary least square (OLS) regression model to explain CO₂ emissions. The empirical results found that the ecological footprint of forest products increases CO₂ emissions. Fossil fuel consumption in the country has increased CO₂ emissions. We also found that the increment in the country’s forest cover has helped reduce CO₂ emissions. Investments from foreign countries were proven to affect pollution negatively. Moreover, China’s tremendous growth has been found to increase its pollution levels. The recent population trend in China is seen to reduce CO₂ emissions according to the results. These findings provide new insights into forestry and climate change and offer theoretical and practical implications to the body of knowledge, as explained below.

5.2. Implications

5.2.1. Theoretical Implications Based on the Faustmann Basic Economics Model

Forests are the backbone of many countries, as forest products are used to earn foreign exchange to improve the living standards of their citizenry. Given this, how can a country maximize its forest income and reduce climate change simultaneously? Various solutions, such as planting new trees to replace lost ones, creating artificial forests, and adopting alternative products other than forest products, have been suggested, but climate issues remain persistent. The study provides a good foundation for future studies on forest economics models and climate change. Forest economics models in recent decades have focused on determining profitably, forest yields, and valuation. Based on the literature review of forest economics models, the development of new models becomes a necessity. To achieve sustainable goals, new models must emphasize sustainability factors that consider both profitability and sustainability. The basic economic model that is mostly applied in forestry is still the Faustmann model. This model has been modified and extended in numerous ways, and the main extensions are in the direction of introduction of risk and the valuation of non-timber forest goods and services. As special attention is being paid to the pricing of carbon to produce additional income for forest owners and using the portfolio theory for forest decisions, climate change issues must be considered. Proper forestry management tools and models that consider climate change issues for sustainability purposes are the way forward in saving our forests and the planet from climate change. These models will not only prioritize maximizing forest products’ profitability but consider climate change issues that are affecting the country and the world at large. The results found in China’s case study provide evidence that climate change can be mitigated when the world economies embrace sustainability. Moreover, scholars can draw upon the model built in this study in the development of new economic models. This will be a great contribution towards the achievement of sustainable development goals in the long run and bridge the gap between theory and practice.

5.2.2. Practical Implications

Based on the results, the following practical implications and recommendations are suggested for policymakers. To begin with, the study’s findings revealed that the ecological footprint of China’s forest products helped reduced CO₂ emissions in the region. Generally, China’s ecological footprints have been high, exceeding its biocapacity. This finding is evidence that when much emphasis is placed on the reduction of forest area depletion, the country’s pollution levels will decrease. In addition, this effort can be transferred to other sectors that contribute to carbon production in the country. Policymakers must initiate policies that guide companies and industries to reduce CO₂ emissions. These contributions will come together to influence the country’s pollution levels in the long run. Second, we found that fossil fuel consumption has contributed to environmental pollution in the region. Due to China’s rapid development and industrialization, these results are not surprising, as companies and various sectors utilize fossil fuel energy. After all, such fuel is cost-effective. The price of penalties for environmental pollution is far more than the price of green fuels. As a result, industries that rely mostly on traditional fuels and energy must be encouraged and compelled to switch to renewable energies or combine the two. A sustainable energy structure policy can be formulated for companies that rely solely on fossil fuel energy in the short run. With education and constant support from the government, companies can gradually embrace green energies and technologies. This will contribute massively towards the achievement of the Chinese goal of a sustainable economy and the world’s sustainable development goals. Third, it is evidenced in the results that forest cover helps reduce CO₂ emissions. China has been increasing its forest cover in the past two decades to mitigate climate change issues. However, the country remains the top CO₂ emitter. This is due to the increasing industrialization that offsets green efforts. The results might have been different if the trees were not planted during increasing economic growth. This indicates that the country needs to continue the addition of new forest cover every year. The existing forest area must be increased or left to grow to reduce emissions. Policymakers must encourage citizens to also embrace tree planting in their communities and backyards. This collective effort will influence the country’s pollution levels to create a sustainable environment for all. Finally, we found that economic growth negatively affects the country’s pollution levels. China’s rapid development over the past decades is admired by many developing countries. The country’s growth has been linked to its industrialization and GDP growth. However, the country must prioritize “growing in green”. This comes with creating industries that produce green products and encouraging citizens to use such products. Additionally, many foreign industries are eager to make investments. Policymakers must prioritize green investments that contribute to sustainable development goals. These investments will boost the economy’s entire green investment towards sustainable development goals.

5.3. Limitations and Future Directions

The study was not devoid of limitations. We believe that some factors may have influenced the results of this study and need to be brought to light. First, due to financial constraints, the study could not access some literature due to “closed access” or the “payment requirement” to access certain papers. This limited the literature able to be reviewed. Second, the study used CO₂ in its assessment of pollution. Recent studies have argued that ecological footprint is a better pollution indicator than CO₂. As a result, future studies can use ecological footprints in their pollution assessment, especially in forest-related studies. Again, the study was based on China and may have generalizability due to differences in weather, climate, and other factors. Future studies can also be applied in different regions or include panel data research that uses many countries for novel results. Finally, due to limited data, the study used limited years in this research. An increased time period may influence the results, and we suggest that future research extend study time periods for new findings.