Analysis of the Dynamic Changes and Driving Mechanism of Land Green Utilization Efficiency in the Context of Beijing–Tianjin–Hebei Synergistic Development

Abstract

Studying the development of land green utilization efficiency and the factors that influence it in the Beijing–Tianjin–Hebei region can improve the distribution of land resources among regions and reinforce interregional integrated planning. By constructing a super-efficiency SBM model, calculating the Malmquist–Luenberger index, and constructing a Tobit model, this study explores the spatial features and temporal variations of land green use efficiency in the Beijing–Tianjin–Hebei region from 2010 to 2022. It also examines the mechanism that drives land green use efficiency in the context of the Beijing–Tianjin–Hebei synergistic development. According to this research, Beijing has consistently had the highest land green usage efficiency and a strong green development strength, whereas Baoding, Xingtai, Handan, and other cities in Hebei Province have lower land green utilization efficiency. According to the geographical dimension, the research area’s land green use efficiency exhibits a pattern of “high in the middle and low in the surroundings”, with Cangzhou, Langfang, and Tangshan standing out in terms of both industrial transformation and ecological building. Based on the results of the driving mechanism of land green use efficiency, it is evident that while the degree of urbanization and population concentration has a negative effect on land green use efficiency, the degree of economic development, industrial synergy, opening up to the outside world, environmental regulation, and ecological output all have positive and promoting associations with it. In summary, increasing the optimization of the economic and industrial structure, bolstering technological innovation and policy coordination, and attaining a harmonious coexistence of the economy and ecology are all essential steps in the process to increase the land green use efficiency in the research area when attempting to achieve the goal of sustainable development in the region.

1. Introduction

Land, a vital resource for human existence and development, has always been essential to the process of urban development and economic growth in China, where the country’s economy is currently experiencing progressive growth. However, China’s economic growth during the previous stage of rapid economic development was marked by high input and high pollution, and the country’s traditional and rough land use methods easily resulted in a large amount of resource waste and ecological damage, which had a direct impact on the urban process and the living standards of its citizens [1].

Urban land use efficiency reflects the overall situation of urban resource input and output, and its core purpose is to reduce the input of land resources and maximize the economic, social, and environmental benefits. Compared with the traditional research on urban land use efficiency, land green use efficiency emphasizes the goal of low consumption and low pollution [2,3], and its main goal is to decrease the amount of land resources used while also reducing the undesirable output during land usage in order to better balance the relationship between ecological preservation and economic development and to better adhere to the idea of green development [4,5].

In recent years, scholars have carried out a large number of studies on land use efficiency, mainly focusing on three aspects: land use efficiency measurement methods, land use efficiency evaluation indexes, and analysis of land use efficiency influencing factors [6]. Land use efficiency measurement methods are mostly based on the parametric method represented by stochastic frontier analysis (SFA) [7,8] and the nonparametric method represented by data envelopment analysis (DEA) [9], which are two types of mainstream methods for quantitative measurement and analysis. SFA emphasizes the significance test of input indicators, which guarantees the rigor of input indicator selection. DEA has the advantages of non-subjective empowerment, independent of the influence of the unit of measurement, and can more effectively calculate relative efficiency [10]. However, the calculation of land use efficiency involves many aspects of indicator data, such as land use type, area, output, or environmental indicators. The accuracy and dependability of the study will be impacted by the inconsistent quality of data statistics and the delays in updating the data in some locations. The super-efficient SBM model has become a significant DEA model complement in this regard. The super-efficient SBM model has the distinct advantage of being able to successfully prevent mistakes brought on by angle selection and slack factors, which makes the land use efficiency calculation results more objective. The super-efficient SBM model can more correctly reflect the true level of land use efficiency than the classic DEA model, lessen the bias brought on by the model’s own constraints, and offer more trustworthy data support for the evaluation of land use efficiency [11].

The existing studies are primarily based on economic, social, and ecological benefits when it comes to the selection of indicators for the calculation of land green utilization efficiency, particularly the selection of indicators for the desired output. The indicators are primarily basic indicators like per capita GDP and the incomes of urban residents, which offer a quantitative basis for the calculation of land utilization efficiency. The current broad research on the effectiveness of green land use is gradually exposing its flaws as social development moves towards the stage of high-quality development and the Beijing–Tianjin–Hebei region becomes more synergistic. Although the basic indicators can reflect a certain level of economic output, it is difficult to accurately capture the urgent demands of the current society for green and innovative development. From the standpoint of green development, traditional indicators are unable to fully assess whether land use is in line with the green path of sustainable development because they rarely consider the degree of clean energy utilization, the recycling efficiency of resources during the land use process, and other emerging points. From an innovation and development standpoint, the current indicator system barely addresses the comprehensive enabling impact of scientific and technological innovation inputs on land use efficiency, neglecting the potential benefits of high-tech industries’ innovative output and the enhancement of land planning accuracy through digital management tools.

However, the majority of current research on regional synergistic growth examines land use indicators for each province and city separately, neglecting to take into account the need for interregional linkage and land resource optimization in the context of Beijing, Tianjin, and Hebei’s synergistic development. This results in a lack of global perspective when developing land use strategies, which hinders the gradual enhancement of the overall land green use efficiency by failing to properly encourage the sensible flow and efficient distribution of land elements in the area.

First and foremost, the majority of the current research examines the issue from the perspectives of land, labor, and capital input components to look into the elements influencing how effectively green land is used. Research related to the efficiency of land justification has also become more extensive, including through industrial structure optimization, policy constraints, and other means of improving land use efficiency. To a certain degree, regional cooperative development can direct the flow of land resources and contribute to the optimization of land use layout. Increasing the share of high-output and low-energy-consuming sectors can effectively reduce resource loss and improve land use efficiency. The consequence of enhancing industrial structure on the efficiency of land usage has been continuously studied. However, the hotspot of current research tends to be the influence of traditional factors on land use efficiency only from a single perspective. The study of land use efficiency in the modern context of green development and high-quality development cannot ignore scientific and technical innovation, which is the main driver of sustainable social development.

Furthermore, the mediated effect model, twofold difference model, Tobit model, and other popular models are among the research methodologies used in the study of the factors impacting land use efficiency. The role of variables like the degree of urbanization and economic development can be examined using these models [12,13,14,15], as well as the structure of industry [16,17], environmental control [18], and the level of scientific and technological innovation [16]. Among them, the Tobit model has an excellent ability to deal with data where the dependent variable is restricted or truncated. In the study of land use efficiency, many influencing factors are not simple linear relationships, and the land use efficiency itself is affected by a variety of constraints. The Tobit model can better adapt to this complex data structure and variable relationships [19]. The Tobit model, for instance, can precisely identify the beneficial impacts of urban economic growth, urban medical care, urban education, and urban economic status on land use efficiency, as well as the negative effects of urban transportation roads and population density. Both the beneficial effects of ecological environment management and economic development, as well as the detrimental effects of land urbanization and population agglomeration, are evident in the study of spatial and temporal patterns of land green use efficiency and its affecting variables in Beijing, Tianjin, and Hebei [19,20]. Besides, the research scale of land use efficiency mainly focuses on provincial units or typical cities with specific characteristics, such as resource cities in the context of industrial transformation, Beijing–Tianjin–Hebei urban agglomeration, and the Yangtze River Basin. Analyzing its changes in different time periods and spatial scales helps to gain insight into the fitness of land use and policy regulation in the region [21,22,23].

To increase regional competitiveness and economic development, China has established metropolitan clusters such as Chengdu–Chongqing, the Yangtze River Delta, and Beijing–Tianjin–Hebei. Among these, the Beijing–Tianjin–Hebei Cooperative Development Plan Outline, which was adopted by the CPC Central Committee’s Political Bureau in April 2015, is significant because it raises the plan to the level of a major national strategy. The plan’s main goals are the orderly dissolution of Beijing’s non-capital functions and advancements in several important areas, including industrial restructuring and upgrading, ecological environmental protection, and Beijing–Tianjin–Hebei economic integration.

With the acceleration of urbanization, the Beijing–Tianjin–Hebei region, as an important development hub in China, has shown a booming trend in urban construction activities, and the demand for land resources has gradually increased. Large-scale infrastructure construction, industrial park development, and urban area extension are only a few of the significant efforts that are part of the Beijing–Tianjin–Hebei coordinated development strategy. However, the Beijing–Tianjin–Hebei region has limited land resources, and as these economic activities have grown, the burden on these resources has increased. The tension on land supply is growing in this area as a result of the conflict between the rigorous enforcement of arable land conservation laws on the one hand and the growing need for building land for urban expansion on the other. Achieving sustainable land use and increasing the effectiveness of green land use are essential in the strategic context of Beijing, Tianjin, and Hebei’s coordinated development. This will guarantee the Beijing–Tianjin–Hebei region’s long-term stable economic and social growth.

Whether or not land can be used in a sustainable and environmentally friendly way as a carrier of social production activities also has an impact on the city’s natural environment. Examining the balance and optimization path of regional coordinated development, enhancing the region’s overall competitiveness, and providing useful references for the coordinated development of China’s urban agglomerations are all made possible by the study of land green use efficiency and its influencing factors in the Beijing–Tianjin–Hebei region. This is due to different cities having distinct patterns of land use, and the policy of coordinated development serves as a backdrop supporting this [24].

In view of this, this study is based on the frontiers of the times and the pulse of the Beijing-Tianjin-Hebei synergistic development, exploring how to use the power of science and technology innovation to improve the efficiency of the green use of land and reduce the output of pollution at the same time. It aims to break through the existing research bottlenecks, fill in the theoretical gaps, provide both prospective and practical scientific guidance for the practice of green land use in the new era, and help regional and even national land resource management to a new level. The following minor contributions are made by this work in light of the earlier research:

(1)

The super-efficient SBM model is chosen because it eliminates subjectivity in the method of deciding on the weights of the comprehensive assessment indexes, clarifies the area that has lower land green use efficiency, and makes the efficiency boundary assessment clearer.

(2)

Addition of the idea of green development to the index system for evaluating land use efficiency, emphasizing the “greening” and “ecology” of the process and the results of land use, choosing the number of patents granted as the output index of technological innovation, and constructing a system that includes ecological damage, environmental pollution and other non-expected outputs in the land use process. The number of patent authorizations is chosen as the output index of technological innovation, and an evaluation index system is constructed that includes non-desired outputs such as ecological destruction and environmental pollution in the process of land use.

(3)

Consideration of the history of synergistic growth between Beijing, Tianjin, and Hebei, dividing the investigation period based on policy nodes, evaluating representative indicators of the influencing elements, and thoroughly exploring the mechanism behind the efficiency of green land use.

2. Materials and Methods

2.1. Study Area

With a population of over 100 million people and a total area of 218,000 km², the Beijing–Tianjin–Hebei region is the most economically developed of North China’s three main metropolitan agglomerations (Figure 1). The GDP of the Beijing–Tianjin–Hebei region in general has shown an increasing trend and a large increase, and the economic growth of Beijing is particularly rapid. The GDP of the Beijing–Tianjin–Hebei region in the first half of 2024 amounted to 5149.2 billion yuan, with a significant year-on-year growth. Among them, Beijing’s GDP in the first half of the year was 2179.1 billion yuan, a year-on-year increase of 5.4%; Tianjin’s GDP was 819.1 billion yuan, a year-on-year increase of 4.9%; and Hebei’s GDP was 2151 billion yuan, a year-on-year increase of 5%. In the collaborative development of Beijing–Tianjin–Hebei, emphasis has been placed on the integration of ecological environmental protection, strengthening ecological restoration and environmental governance, and creating good ecological conditions for the green use of land. In the past, rough urbanization and industrialization encroached on a large amount of ecological space, resulting in the integrity and connectivity of ecosystems being damaged, and the ecological function and natural purification capacity being seriously weakened. Due to historical reasons, the industrial structure of Beijing–Tianjin–Hebei and neighboring areas is dominated by heavy chemicals, with a large pollution base, and the task of adjusting the structure and curing pollution is arduous. In some areas, there are still some polluting enterprises working day and night and discharging secretly and directly, posing a serious threat to the water environment.

Between 2015 and 2022, there was a significant decline in grassland and a significant increase in development land following the publication of the Beijing–Tianjin–Hebei Cooperative Development Plan. To strengthen regional ties, improving transportation and other infrastructure networks has become a top priority, with a large amount of land being used to build railroads, highways, and other transportation facilities. Concurrently, industrial areas have been established and widened as a result of the synergistic development of industries, and the optimization of urban functions and quality enhancement also necessitates the expansion of construction land. These factors collectively contribute to the notable rise in construction land. Some meadows have been created and used during the industrial transfer and upgrading process to accommodate the demands of urban expansion and the construction of infrastructure.

2.2. Data Sources

The period of 2010–2022 is selected as the study period, as although the Beijing–Tianjin–Hebei region did not elevate the synergistic development into a national strategy in 2010, the results of the previous development have laid the foundation for the subsequent work. Following the release of the Beijing–Tianjin–Hebei Synergistic Development Plan Outline in 2015, land use was modified in accordance with this policy, allowing for a more thorough analysis of the direct shift in land green use efficiency. In 2019, the Beijing-Tianjin-Hebei coordinated development strategy was fully implemented, and Beijing’s non-capital city achieved results in relieving its functions. Significant advancements in synergistic development and policy consequences are evident in a number of fields. Synergistic development has made substantial progress, and the effect of this policy is apparent. At this time, the relationship between changes in land green utilization efficiency and its driving factors can be explored. The year 2022 was an important point in the “14th Five-Year Plan” period when the Beijing–Tianjin–Hebei coordinated development was pushed forward, which showed the latest results and development trends, presented the final evolution of land green utilization efficiency under the promotion of long-term policies, and provided a scientific basis for future development.

Based on this, this paper collects relevant data on the study area from 2010 to 2022. The calculation data and the screening of influencing factors of the indicators related to the land green use efficiency mainly come from the China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, statistical yearbooks of Beijing–Tianjin–Hebei municipalities, and statistical bulletins of socio-economic development, and the individual data (2010) are collected by contacting the government departments and the government of Beijing. Individual data (green space area of Beijing in 2010) were obtained by contacting relevant government departments, and some data (evaluation indicators of land green utilization efficiency and indicators of influencing factors) were calculated from existing data. Given that land use efficiency is presented as mean data, this study uses the ratio of each indicator to the total land area or the total population in the calculation of each type of indicator. The administrative boundaries of each administrative unit were obtained from the 1:4 million database of the National Center for Basic Geographic Information (https://ngcc.cn/dlxxzy/gjjcdlxxsjk/, accessed on 20 January 2025.).

2.3. Research Methods

2.3.1. Land Green Utilization Efficiency Index System

Land green utilization is a comprehensive way of non-polluting, sustainable, saving, and intensive land use under the concept of green development, so land green utilization efficiency indicators should be screened from various aspects (as shown in

Table 1. Building the Beijing–Tianjin–Hebei region’s land green use efficiency assessment index system.

Primary	Secondary Indicators	Tertiary Indicators
Input indicators	Capital	Fixed capital investment in unit area (billion yuan/km2)
	Land	Per capita road area
		Proportion of built-up land area
	Labor	Number of employees per unit land (persons/km2)
Desired outputs	Social and economic benefits	Retail sales of social consumer goods per unit land (billion yuan/km2)
		Output of secondary and tertiary industries in unit land (billion yuan/km2)
	Technological innovation	Number of municipal patents authorized (piece)
	Ecological environment	Per capita green space
Undesired outputs	Consequences of negative environmental effects	Industrial sulfur dioxide emissions per unit land areas (ton)
		Industrial particulate emissions per unit land areas (ton)
		Industrial smoke emission per unit land areas (ton)

) [19].

Road area per capita, built-up area ratio, per capita fixed asset investment, and per capita number of employees are samples of input indicators that are selected from land, capital, and labor. Fixed asset investment directly reflects the scale of capital injected into the land, reflecting the degree of economic resources invested in the development and utilization of land. Road area per capita can measure the occupation of land for infrastructure construction, and the proportion of the built-up area can intuitively show the degree of expansion of urban land, which together reflect the input status of land resources in urban construction. The number of employees per capita serves as the labor input, and this statistic is an important measure of the human activity that the land supports.

Desired output indicators are selected from socio-economic benefits, technological innovation benefits, and ecological benefits. Key indicators of the economic benefits brought about by the use of land are the average output value of secondary and tertiary industries and the average retail sales of consumer goods (in billions of yuan). These two metrics amply demonstrate the land’s capacity to generate economic value. Given the significance of scientific and technological innovation as a catalyst for social progress, the number of patents awarded is selected to reflect the benefit of technological innovation. The amount of green space per person, which is a crucial component of the ecosystem whose size is directly correlated with the ecological impact of land usage, is used to quantify the ecological value.

The non-desired output indicators—per capita industrial sulfur dioxide emissions, per capita industrial particulate emissions (tons), and per capita industrial soot emissions (tons)—can demonstrate how industrial activities have a detrimental effect on the environment throughout the land use process when evaluating the efficacy of green land use. By carefully choosing indicators, a comprehensive assessment of land green use efficiency can provide a scientific basis for land use decision-making.

2.3.2. Super-Efficiency SBM Model

This article defines urban area green utilization efficiency through the lens of Beijing–Tianjin–Hebei’s “coordinated development”, seen as the minimization of energy and land resource inputs while sustaining economic growth and optimizing desired outputs, such as ecological, social, and economic benefits, through the judicious allocation of industrial structure and the optimization of land-use structure while containing the rise of undesirable outputs, such as the increase of unwanted emissions, like SO₂ and pollutants, in an attempt to increase the quantity of green land apply.

The land use process is characterized by multiple inputs and multiple outputs, and in order to make the evaluation results more objective [23], the super-efficient SBM model is selected to calculate the land use efficiency. The traditional data envelopment analysis (DEA) is a typical efficiency evaluation method, which has the advantages of not needing to set the production function artificially, utilizing the linear programming method for the operation, and avoiding the estimation limitation caused by the measurement problems such as mis-setting the function form and non-efficiency terms.

The super-efficient SBM model is different from the DEA model in that it is suitable for the actual land use in the region and is based on the idea of variable returns to scale, which can handle multiple inputs, multiple outputs, and non-efficiency components. Input indicators, such as land resources and energy, and output indicators, like economic, social, and ecological advantages, are chosen as parameters in this study based on the data characteristics of the area. In the meantime, the original model serves as the foundation for the super-efficiency SBM model, which successfully resolves the sorting issue by removing itself from the reference when assessing a particular decision-making unit, allowing its efficiency to exceed 1 [25,26]. Therefore, in this paper, the non-expected output super-efficiency SBM model is selected to comprehensively analyze and calculate the land green use efficiency from a static perspective. The model is as follows:

$$\mathrm{m}\mathrm{i}\mathrm{n} \rho ={\displaystyle \frac{1+\frac{1}{m}{\sum }_{i=1}^m\frac{s_i^{-}}{x_{i0}}}{\begin{array}{c}1-\frac{1}{q_1+q_2}\left[\frac{{\sum }_{r=1}^{q1}{s}_r^{+}}{y_{r0}}+\frac{{\sum }_{t=1}^{q2}{s}_t^{b-}}{b_{t0}}\right]\\ \end{array}}}$$

(1)

$$\mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}\sum _{j=1,j\ne j_0}^n{x}_j{\lambda }_j-s_i^{-}\le x_{i0},(i\equiv \mathrm{1,2},\cdots ,m)\\ \sum _{j=1,j\ne j_0}^n{y}_j{\lambda }_j+s_t^{+}\le y_{i0},(r\equiv \mathrm{1,2},\cdots ,q_1)\\ \sum _{j=1,j\ne j_0}^n{b}_j{\lambda }_j-s_t^{b-}\le b_{i0},(t\equiv \mathrm{1,2},\cdots ,q_2)\\ 1-{\displaystyle \frac{1}{q_1+q_2}} [{\displaystyle \frac{{\sum }_{r=1}^{q1}{s}_r^{+}}{y_{r0}}}+{\displaystyle \frac{{\sum }_{t=1}^{q2}{s}_r^{b-}}{b_{t0}}}]>0\\ {\lambda }_j,s_i^{-},s_r^{+},s_t^{b-}\ge 0,(j\equiv \mathrm{1,2},\cdots ,n,j\ne j_0)\end{array}\right..$$

(2)

The efficiency value is represented by $\rho$ in the formula, while the numbers of inputs, desirable outputs, and undesirable outputs are indicated by $m$, $q_1$, and $q_2$, respectively. The comparatively efficient decision-making units are denoted by the variable $j$, and the total number of these units is denoted by the variable n. Furthermore, $x_j,y_j,$ and $b_j$ stand for the decision-making unit $j$’s input, desirable output, and undesirable output variables, respectively. The weight vector linked to the decision-making unit is denoted by ${\lambda }_j$. The slack variables for inputs, desired outputs, and undesired outputs are denoted by the variables $s_i^{-},s_r^{+}$, and $s_t^{b-}$, respectively. Additionally, the assessed decision-making unit’s original inputs, desired outputs, and unwanted outputs are indicated by the symbols $x_{i0},b_{t0}$, and $y_{r0}$, respectively. When $\rho \ge 1$, the decision-making unit is considered relatively efficient; conversely, when $\rho <1$, the unit is deemed relatively inefficient. A larger value of $\rho$ indicates a higher level of efficiency for the decision-making unit.

2.3.3. Malmquist–Luenberger Model

Although the super-efficient SBM model’s calculation of land green utilization efficiency can accurately gauge the effective degree of inputs and outputs on land resources, the outcome only compares efficiency over a single time period and is unable to show the dynamic process of land green utilization efficiency. The Malmquist index is often used in the calculation of total factor productivity to get the change of land green use efficiency in different decision units. Therefore, this method is widely used in long time series research [27]. By using a local reference set to illustrate the dynamic process of the total factor productivity of green land use in the decision-making unit, the Malmquist–Luenberger (ML) index, a new function derived from the Malmquist index, can efficiently analyze the impact of undesirable outputs on the efficiency of green land use [28]. This study is based on the long-term continuity to take into account the influence of technical factors on the land green use efficiency; therefore, the ML index model is used to analyze the land green use efficiency in the Beijing–Tianjin–Hebei region from a dynamic perspective, aiming to improve the scientific rationality of the study. Assuming that the returns to scale do not become premised, the specific model setup is as follows:

$$\begin{array}{cc}M\left({\mathrm{x}}^{\mathrm{t}},y^t,x^{t+1},y^{t+1}\right)& ={\left(M\times M_{t+1}\right)}^{\begin{array}{c}1\\ 2\end{array}} =[{\displaystyle \frac{D^t(x^{t+1},y^{t+1})}{D^t(x^t,y^t)}} \times {\displaystyle \frac{D^{t+1}(x^{t+1},y^{t+1})}{D^{t+1}(x^t,y^t)}}{]}^{\begin{array}{c}1\\ 2\end{array}}\\ & ={\displaystyle \frac{D^{t+1}(x^{t+1},y^{t+1})}{D^t(x^t,y^t)}} \times [{\displaystyle \frac{D^t(x^t,y^t)}{D^{t+1}(x^t,y^t)}} \times {\displaystyle \frac{D^t(x^{t+1},y^{t+1})}{D^{t+1}(x^{t+1},y^{t+1})}}{]}^{\begin{array}{c}1\\ 2\end{array}}\end{array}$$

(3)

In the formula, $D^t(x^t,y^t)$ and $D^t(x^{t+1},y^{t+1})$ represent the distance functions of the decision-making unit in periods $t$ and $t+1$, respectively, when the technology from period $t$ is used as the reference. Similarly, $D^{t+1}(x^t,y^t)$ and $D^{t+1}(x^{t+1},y^{t+1})$ denote the distance functions of the decision-making unit in periods $t$ and $t+1$, respectively, when the technology from period $t+1$ serves as the reference. The term $M({\mathrm{x}}^{\mathrm{t}}$,${ y}^t$,${ x}^{t+1},y^{t+1})$ indicates the degree of change in urban land use efficiency of the decision-making unit from period $t$ to period $t+1$. If $M>1$, it indicates an improvement in the level of urban land use efficiency; conversely, if $M<1$, it indicates a decline in efficiency.

$$ML=EC\times TC=PEC\times SEC\times TC$$

(4)

Efficiency change (EC) in land use and technological change (TC) are the two parts of the Malmquist–Luenberger (ML) index that can be broken down using the formula. Scale efficiency change (SEC) and pure technological efficiency change (PEC) are two subcategories of efficiency change (EC). The technological efficiency of the decision-making unit increases when EC is larger than 1; on the other hand, an EC value smaller than 1 implies that the current technology has not been properly used. Technological advancement inside the decision-making unit is implied when TC is greater than 1; technological regression is indicated when TC is less than 1.

2.3.4. Tobit Model

Regarding the coordinated growth of Beijing, Tianjin, and Hebei, there is a close logical relationship between land green utilization efficiency and the economy, industry, opening up to the outside world, urbanization of the population, level of environmental control, and level of ecological output. A more reasonable economic structure supports the development of the land structure in the direction of green intensification, and the coordinated development of Beijing, Tianjin, and Hebei encourages the integration of the regional economy, which offers an economic impetus to improve the efficiency of green land use. Coordinated development encourages industrial upgrading and transfer at the industrial level, which leads to the best possible distribution of land resources among various sectors, the removal of land for underdeveloped production capacity, and a rise in the green use efficiency of industrial land. The increased openness to the outside world has drawn top-notch resources and cutting-edge ideas from around the world, which has led Beijing, Tianjin, and Hebei to utilize international green standards in land development and usage and raise the overall green level of land use. In the process of population urbanization, Beijing, Tianjin, and Hebei’s coordinated development serves to direct the population’s reasonable distribution, prevent excessive land pressure concentration, promote the transition of urban construction to a green mode, and enhance the carrying capacity of land and the efficiency of green utilization. The level of environmental control has been unified and strengthened under the synergistic development strategy, and the strict control has prompted the land use process to pay more attention to environmental protection, reduce polluting land use, and realize green development. At the level of ecological and environmental governance, environmental control effectively reduces pollution emissions in production and life, forces enterprises to innovate in green technology, and increases the output of green economic benefits. The increase in the level of ecological output stems from the expansion of the area of regional ecological land, and the ecological land has the function of degrading pollution, maintaining biodiversity, and also improves the quality of the ecological environment of the neighboring regions, which has a spillover effect [29] (as shown in

Table 2. Influencing factors of land green use efficiency.

Criterion Layer	Indicator Layer	Variable Explanation
Economic level	Level of economic synergy (X1)	Gross domestic product (GDP)/total population
	Level of industrial synergy (X2)	Value of the tertiary output/value of the secondary output
	Level of opening-up to the outside world (X3)	Number of foreign-invested enterprises/number of industrial enterprises
Social level	Population urbanization (X4)	Population of municipal districts/population of the whole city
	Degree of population aggregation (X5)	Total population/land area
Ecological environment Governance level	Level of environmental regulation (X6)	Treatment rate of general industrial solid waste
	Level of ecological output (X7)	Per capita green space area

).

This study uses the efficiency value determined by the super-efficient SBM model as the dependent variable and the filtered influencing factors as the independent variables for the regression analysis in order to thoroughly examine the driving mechanism of land green use efficiency in the Beijing–Tianjin–Hebei region [29,30,31]. The Tobit model is often used to investigate how the dependent variable, which is partially continuously or discretely distributed, changes under the influence of some factors, and a restricted variable is the dependent variable. The land green use efficiency value of Beijing–Tianjin–Hebei land measured by the super-efficient SBM model is discrete and has a significant truncation phenomenon. In particular, it is emphasized that if there is serious multicollinearity between independent variables, it will lead to a large deviation in the model’s ability to explain the evaluation indexes of urban land use efficiency and synergistic development. As a way to assess the multicollinearity of the linear relationship between the influencing elements of Beijing–Tianjin–Hebei land green use efficiency, this paper adopts the variance inflation factor test (VIF test). After the multicollinearity test is passed, the Tobit model introducing the truncated tail regression is used to analyze the factors, and the model is set as follows:

$$Y^{\mathrm{\ast }}=X_i\beta +{\epsilon }_i$$

(5)

$$Y=\left\{\begin{array}{c}\alpha +\beta X+\epsilon ,Y>0\\ 0,Y\le 0\end{array}\right.$$

(6)

In the regression model, $X$ denotes the independent variable, $Y$ signifies the censored dependent variable, $\beta$ represents the regression parameter, $\alpha$ is the constant term, and the error term $\epsilon$ is assumed to follow a normal distribution $\epsilon -N\left(0, {\sigma }_2\right)$.

3. Results

3.1. Time Series Changes in Land Green Utilization Efficiency in the Beijing–Tianjin–Hebei Region

Overall, the trend of land green utilization efficiency changes varies among prefecture-level cities in the Beijing–Tianjin–Hebei region (See Figure 2). Before 2015, the Beijing–Tianjin–Hebei region experienced rapid economic development and large inputs of land resources, which led to higher non-desired outputs such as environmental pollution and industrial pollution. The land-averaged industrial sulfur dioxide in Shijiazhuang, for instance, was as high as 8.703, which led to a region-wide decreased land green usage efficiency. Through the promotion of ecological civilization construction policies, the study area collaborates to promote the simultaneous development of the ecological environment and economic construction. In comparison to 2010, the average industrial wastewater decreased by 5.54% in 2015. In the 2015–2019 period, built-up area greening coverage increased by nearly 4%, which increased the desired outputs while decreasing the non-desired outputs, and the land green utilization efficiency increased significantly.

Beijing’s land green utilization efficiency has always been in the leading position and has shown a steady upward trend. It continued to rise from 1.6176 in 2010 to 2.3089 in 2019 and remained at a high level in 2022 despite a slight decline. As the capital of the country, it has a high level of economic development and an industrial structure dominated by high-tech industries, modern service industries, etc.; therefore, there are more funds and technologies invested in the green development and utilization of land. Beijing’s land green usage efficiency index grew more quickly between 2010 and 2015 as a result of various factors, including improved infrastructure building and urban planning, stronger environmental regulations, and economic restructuring. Improvements in economic and environmental indicators have resulted from the implementation of pertinent policies in numerous areas. Changes are not readily apparent after 2015 because of the stabilization of the economic transition, environmental governance hurdles, and restrictions on land use and urban space. The changes were not immediately apparent, and there was little room for additional quality improvement and improvement of environmental indicators until 2015, when economic transformation entered a steady phase and environmental governance ran into bottlenecks due to land use and urban space limits.

All things considered, Tianjin’s land’s green use efficiency is comparatively constant, staying at around 1.0 with minimal variation. As an important industrial city and port city, Tianjin has a relatively traditional industrial structure, and in the process of transformation and upgrading, the green utilization efficiency of land improves slowly.

During the study period, the land green use efficiency of the prefecture-level cities in Hebei Province was mainly divided into two categories: Baoding, Xingtai, Handan, and Qinhuangdao had lower land green use efficiency, and most of them did not reach the effective status in the four study years; Cangzhou, Tangshan, Zhangjiakou, Chengde, and Hengshui reached the effective status, and Langfang and Shijiazhuang also reached the effective status in most of the study period years, except for the lower efficiency in some years. Cangzhou, Langfang, Tangshan, and other cities located near Beijing and Tianjin have a good industrial base, especially after 2014, in the context of non-capital function relocation, and they have better taken over all kinds of industries in Beijing, which makes the per capita economic output higher, and at the same time, they have strengthened the eco-construction and environmental management, especially Langfang, where the land green use efficiency has improved significantly. However, some cities in Hebei Province are dominated by traditional industries, such as Baoding, Handan, and Xingtai, which have problems of high resource consumption and environmental pollution that affect the green utilization of land.

3.2. Spatial Dynamics of Land Green Utilization Efficiency in the Beijing–Tianjin–Hebei Region

The efficiency numbers are separated based on the computation results into low efficiency (<0.5), medium efficiency (0.5~1.0), effective (1.1~1.5), high efficiency I (1.5~2.0), and high efficiency II (>2.0), and the cities’ land green use efficiency is ranked annually. After that, the geographical features of the land’s green use efficiency in the Beijing, Tianjin, and Hebei region are examined. As can be seen in Figure 3, the overall land green utilization efficiency shows the spatial characteristics of “high in the middle and low in the surrounding area”, and it has changed from a high-efficiency city with single development in Beijing to a high-efficiency city cluster led by Beijing. From 2010 to 2019, Beijing’s land green use efficiency level increased annually, rising from effective to high efficiency II, with obvious growth, mainly due to Beijing’s investment in environmental protection and ecological construction, as well as its efforts in industrial restructuring and scientific and technological innovation. According to the Beijing Urban Overall Plan (2016–2035) and other plans, Beijing has clarified the ecological construction goals and tasks, which provides scientific guidance and a planning basis for the green utilization of land [32]. In 2022, the economic situation, the social environment, policy adjustments, and other factors, especially the coronavirus pandemic led to a reduction in economic activities and personnel movement, which had a desired output of land use efficiency and a certain negative impact, indicating that Beijing’s land green usage efficiency had somewhat declined. Tianjin’s land green utilization efficiency has consistently been at a comparatively steady level, suggesting that the city has successfully balanced its economic growth with the conservation of the environment.

The land green utilization efficiency of the cities around Beijing, Tianjin, and Hebei was primarily in the effective stage in 2010, as can be shown from the spatial distribution chart. Influenced by Beijing’s high-efficiency radiation, Chengde, Zhangjiakou, Tangshan, and Langfang have better green development. Chengde City, as a Beijing–Tianjin–Hebei water conservation area, has a unique environmental advantage. Chengde, together with Zhangjiakou, is an ecological environment support area in the northwest of Beijing–Tianjin–Hebei and has actively carried out various green ecological projects, relying on the Beijing–Tianjin–Hebei wind and sand management and the dam afforestation project, among others, to improve the urban ecological environment and increase the efficiency of green use of city space. However, from 2015 to 2019, the southwestern region of Hebei province, with cities such as Baoding, Shijiazhuang, Xingtai, and Handan, had abnormal land green utilization efficiency values, all of which are in the inefficient stage and have low-efficiency values. As the capital city of Hebei Province, in Shijiazhuang City the scale of investment in key projects has been expanding, providing a strong impetus for economic development, but at the same time, industrial sulfur dioxide in Shijiazhuang City has reached the highest level in the province, and the haze problem has become a major criticism of the region. Local governments and enterprises overly pursue short-term economic benefits in the development process, ignoring the importance of green development and sustainable development, resulting in low efficiency of green land use. Baoding City relies on its superior geographical location, constituting a golden triangle with Beijing and Tianjin City, and has become an important transportation hub in North China, coupled with its good industrial base and rapid economic development. However, the economic boom associated with high industrial three-waste indicators and a green space coverage rate of only 38.41% has led to an insufficient level of land green use efficiency. Although Xingtai City implemented a number of policies and measures to enhance the effectiveness and quality of land usage, inadequate supervision resulted in poor policy implementation, which had an impact on the improvement of land green use efficiency. Xingtai City’s land use efficiency performed poorly between 2015 and 2019.

After analyzing the policy and current situation of Handan, it is found that the city, as a former resource-based city known as the “Coal City and Steel Capital”, has a solid industrial foundation and rich mineral resources, which have established a solid basis for its economic growth. The city’s powerful manufacturing sector and abundant natural resources have provided a solid basis for its economic growth. Nevertheless, because of the traditional industry’s shortcomings, industrial sulfur dioxide and soot emissions are extremely high, and the main obstacles to increasing the effectiveness of the green use of its land may be industrial structural adjustment and environmental pollution control. It is challenging to transition some conventional high-polluting sectors throughout the industrial restructuring process, which has an impact on the efficient use of land.

3.3. Dynamic Evolution Analysis of Land Green Utilization Efficiency in the Beijing–Tianjin–Hebei Region

According to the Beijing–Tianjin–Hebei region’s total factor productivity (TFP) data study from 2010–2022, the areas with the biggest shifts are concentrated in a few key cities (Figure 4). First, Baoding City’s TFP jumped from 0.6813 to 1.4877 between 2015 and 2019. This increase can be ascribed to a number of policies the local government put in place to support investment and innovation, as well as a rise in infrastructure development that aided in economic transformation. However, the fallback of TFP to 0.8688 during 2019–2022 indirectly reflects the challenges posed by uneven resource allocation and changes in the external economic environment after rapid development.

Total factor production (TFP) changes in Cangzhou City exhibit a broad range of oscillations, first rising and then declining. The significant increase in the TFP in Cangzhou City during 2015–2019 may be closely related to the promotion of several policies and industrial transformation. During this period, the local government implemented policies to strengthen innovation and investment, which promoted the development of new industries, especially the investment in modern services and high-tech industries, and enhanced overall economic efficiency. Cangzhou City, with its favorable geographical location and proximity to Beijing and Tianjin, has gained more resources and opportunities in regional economic cooperation, which has contributed to the rapid growth of the TFP. However, since the beginning of 2019, Cangzhou City’s TFP has dropped sharply to 0.4324, and the sustainability of economic growth has been impacted by changes in the external economic environment as well as the coronavirus pandemic, and trade friction and uncertainty in market demand have led to greater pressure on the survival of enterprises. After experiencing rapid development, Cangzhou City has also experienced the problem of irrational resource allocation, especially in environmental governance and resource utilization, where the contradiction between over-exploitation and environmental protection has intensified, further affecting production efficiency.

Tangshan’s sustained TFP growth indicates that it has achieved remarkable results in industrial restructuring and technological progress. The city has specifically been able to strike a balance between environmental regulation and economic efficiency thanks to its initiatives to support the modernization and transformation of old businesses as well as the emergence of a green economy. Comparatively, Zhangjiakou showed a sharp decline from its 2010–2015 peak, with TFP falling from 2.9537 to 0.5, which reflects the city’s vulnerability to resource utilization and industrial homogenization, and may face the dual challenges of resource depletion and environmental constraints.

Tianjin’s TFP declined to 0.8752 over the 2015–2019 period, demonstrating its shortcomings in economic structural transformation and policy adaptation, which are exacerbated by the external competition it faces, particularly in the process of upgrading its port economy and industries.

Overall, these cities’ TFP fluctuations illustrate the challenges of resource allocation and industrial structure optimization that need to be addressed in order to support economic transformation and high-quality development, in addition to reflecting the effects of policy implementation and the cities’ respective stages of economic development. It is advised that policy synergies are reinforced, technical innovation encouraged, the industrial chain optimized, and overall economic efficiency raised in order to attain sustainable development in the area and guarantee a competitive edge in the future.

The analysis of technical efficiency (EC) data from the study area from 2010 to 2022 reveals significant differences and changes in technical efficiency among cities in the region (as shown in Figure 5). The technical efficiency of Beijing, Tangshan City, Langfang City, and Cangzhou City consistently remains at 1, indicating that these cities are relatively efficient in technology application and resource allocation and possess strong economic resilience and competitiveness. Baoding’s EC was 0.8283 in 2010–2015, and although it improved to 1.2073 in 2015–2019, it fell back to 0.9217 in 2022, with a slight impact on its technological innovation and application capacity. In contrast, Zhangjiakou and Chengde cities show volatility in technical efficiency, especially Zhangjiakou City, which fell to 0.8169 in 2015–2019, reflecting its shortcomings in industrial structure and resource allocation. Shijiazhuang and Qinhuangdao also showed a declining trend in technical efficiency, especially to 0.7678 and 0.744 during 2015–2019, suggesting that these two cities face challenges in the process of technological adaptation and economic transformation. Overall, these data emphasize the need for greater policy coordination and technological innovation in the research area in terms of improving regional technical efficiency in order to reach a greater degree of sustainable economic development.

The analysis of technical progress (TC) data for the region from 2010 to 2022 demonstrates the substantial discrepancies and trends in the performance of cities in the overall region in terms of technical progress (as shown in Figure 6). Baoding’s TC was 0.8225 during 2010–2015 and then rose to 1.2322, indicating that the city has made breakthroughs in technological innovation and application, enhancing the endogenous momentum of economic development. Beijing’s TC remains at a high level, reflecting its continued technological advantage and economic resilience. In contrast, Zhangjiakou and Chengde show large fluctuations in technological progress, especially Chengde’s TC, which dropped to 0.2806 in 2019, showing its serious problems in industrial transformation and resource utilization efficiency. In addition, Cangzhou’s TC increased dramatically to 3.8279 in 2015–2019 before plummeting to 0.4324, which shows the resource pressure and lack of technological adaptation it faces after rapid development. Overall, these data demonstrate the imbalance in technological progress in the Beijing–Tianjin–Hebei region, emphasizing the need to improve overall technological efficiency through policy coordination and industrial upgrading.

The analysis of pure technical efficiency (PEC) and scale efficiency (SEC) in the Beijing–Tianjin–Hebei region from 2010 to 2022 illustrates substantial discrepancies in technology application and economies of scale among different cities (as shown in Figure 7 and Figure 8). The PEC and SEC of Beijing, Tangshan, Langfang, and Cangzhou remain at 1, indicating that these cities show high efficiency and stability in technological innovation and resource allocation. Baoding’s PEC was 0.9507 in 2010–2015, it rose slightly to 1.0519 in 2015–2019, but fell back to 0.9348 in 2022, showing the volatility of its technology application. Zhangjiakou had a more pronounced change in PEC, falling to 0.8517 in 2015–2019, reflecting its challenges in technological progress and economies of scale. Overall, the results of the PEC and SEC data in the region demonstrate the imbalance between the cities in terms of technological efficiency and economies of scale, suggesting the need to strengthen technological innovation and scale optimization to promote high-quality growth in the regional economy.

3.4. Driving Mechanisms of Land Green Utilization Efficiency in the Beijing–Tianjin–Hebei Region in the Context of Collaborative Development

The multicollinearity relationship between the influencing elements of land green usage efficiency in the region against the backdrop of synergistic development is tested in this article using the variance inflation factor (VIF test).

Table 3. Collinearity test.

Model	Standardized Coefficient/Beta	t	Significance	Collinearity Statistics
				Tolerance	VIF
(constant)		29.707	0
Zscore: X1	0.972	3.269	0.002	0.119	8.429
Zscore: X2	0.15	0.802	0.426	0.299	3.35
Zscore: X3	0.401	1.681	0.099	0.184	5.434
Zscore: X4	−0.649	−2.476	0.017	0.153	6.552
Zscore: X5	−0.309	−2.546	0.014	0.711	1.407
Zscore: X6	−0.934	−0.653	0.517	0.005	1.952
Zscore: X7	0.395	0.270	0.789	0.005	2.045

indicates that there is no multicollinearity and that each influencing factor’s VIF value is less than 10, which may be utilized for the subsequent examination of the mechanism behind the efficiency of land green utilization.

The Tobit model’s results (see

Table 4. Regression results of influencing factors on land green use efficiency.

Variable	Regression Coefficient	Estimated Standard Error	Z Value	p Value
Level of economic synergy (X1)	0.2159 ***	0.0589	3.6600	0.001
Level of industrial synergy (X2)	0.0427	0.0409	1.0500	0.301
Level of opening-up to the outside world (X3)	0.0847 *	0.0485	1.7500	0.087
Population urbanization (X4)	−0.1670 **	0.0562	−2.9700	0.005
Degree of population aggregation (X5)	−0.0704 **	0.0249	−2.8300	0.007
Level of environmental regulation (X6)	0.0076 **	0.0111	0.6800	0.050
Level of ecological output (X7)	0.0035 *	0.0275	0.1300	0.090
Constant term	0.6904	0.0225	30.59	0.000

*, **, *** are significant at 10%, 5% and 1% respectively.

) show a substantial positive correlation between the degree of economic synergy and the efficiency of land green use (regression coefficient 0.2159 ***, p-value 0.001). One of the main factors propelling the increase in regional land green use efficiency is the region’s thriving economic development. In the process of economic development, the injection of capital has laid a solid foundation for the development and utilization of land resources. For instance, significant funds have been allocated to ecological restoration initiatives and environmental protection technology research and development, both of which have improved the efficiency of land green usage. At the same time, advanced management concepts and cutting-edge technological means are used in a two-pronged manner to optimize the land use structure in the Beijing–Tianjin–Hebei region. This not only reduces the phenomenon of resource waste but also strongly guarantees the development of land use in the direction of high efficiency and green usage.

The degree of industrial synergy and the efficiency of land green usage are positively correlated (regression coefficient 0.0427, p-value 0.301), but the significance is not obvious. The industries in the region are gradually transforming from traditional high-pollution and high-energy-consumption industries to high-end manufacturing, modern service industries, and green industries, and the land use mode has undergone profound changes. High-end manufacturing and modern service industries in Beijing–Tianjin–Hebei, with their intensive land use characteristics, can create more considerable economic value within a limited land area and significantly lessen the adverse effects on the environment. In particular, the green industry, which prioritizes sustainable growth and ecological preservation, helps to boost the effectiveness of land green use in the region and gives regional land management a boost.

The level of opening up to the outside world is positively related to the land green use efficiency (regression coefficient 0.0847 *, p-value 0.087). The region has benefited from multifaceted development opportunities brought about by increased openness to the outside world. These opportunities include advanced technology, seasoned management experience, and ample capital. This has greatly increased the local land green use effectiveness. Through the introduction of advanced foreign environmental protection technologies and scientific land use management models, the Beijing–Tianjin–Hebei region has made great progress in green land development and utilization. More importantly, opening up to the outside world has given a strong impetus to local enterprises in the study area to actively participate in international competition, prompting them to improve the efficiency of resource utilization from the perspective of their own development strategies in order to better adapt to the increasingly stringent environmental protection requirements of the international market.

Population urbanization, the degree of population concentration, and the efficiency of green land use are significantly negatively correlated (regression coefficients of −0.1670 ** and −0.0704 **, p-values of 0.005 and 0.007). Due to the high population density in Beijing, Tianjin, and Hebei’s urban areas, a significant amount of land resources have been developed and used to support infrastructure development, industrial production, and urban population, resulting in a rapid expansion of the urban space. This growth eventually consumed nearby forest land, grassland, wetlands, and other ecological land, resulting in a sharp decline in the area of ecological land. This resulted in a number of severe ecological problems, including environmental pollution, the rise of the “big city disease”, and a significant challenge to the land resources of the region. The region’s land resources are becoming increasingly stressed due to excessive population concentration, which also significantly increases the pressure on land development. This has a detrimental effect on the effectiveness of green land use and has emerged as one of the major issues that needs to be resolved in the current land resource management system.

There is a significant and positive correlation between the degree of ecological output, the degree of environmental management, and the effectiveness of green land use (regression coefficients of 0.0076 ** and 0.0035 *, p-values of 0.05 and 0.09). By enforcing environmental controls, such as the creation of stringent regulations on land pollution prevention in the region and land ecological protection standards, government agencies have significantly contributed to a more scientific and logical land use behavior. Under this strict regulatory environment, enterprises and individuals in the research area have responded positively by taking the initiative to reduce irrational land development and pollution behaviors, actively exploring ways to reduce the excessive use of pesticides and fertilizers in agricultural production, and strengthening environmental protection measures, such as pollution control, in industrial land use, thus effectively improving the efficiency of green land use [33,34,35,36]. These programs demonstrate the government’s leadership in managing land resources and the value of environmental control in boosting the effectiveness of green land use. The year-on-year increase in green space per capita in the study area has a positive impact on land green utilization efficiency. In the ecological function dimension, the increase in green space per capita significantly improves air purification, climate regulation, and soil and water conservation, creating a better ecological environment for land resources. In the socio-economic benefit dimension, it can effectively enhance land value, encourage the growth of ecotourism, and further improve the health and productivity of the residents, providing a broader space for diversified use of land resources. In the ecosystem diversity support dimension, the primary factor in increasing the efficacy of land green use is the growth in green space per capita. A comprehensive and strong ecological foundation for the sustainable management of land resources in the region is provided by the increase in green space per capita, which also offers rich habitats for a variety of organisms, strongly encourages the ecosystem’s positive cycle, and essentially ensures the improvement of land green use efficiency.

4. Discussion

This study shows that economic synergy, industrial synergy, and opening up to the outside world have different degrees of positive impacts on the land green use efficiency of Beijing–Tianjin–Hebei, which provides important insights into regional development strategies. In terms of economic synergy, the Beijing–Tianjin–Hebei region should further break down administrative barriers and strengthen economic cooperation between cities in the region [37]. In some cases, it can achieve resource sharing and industrial complementarity and facilitate the greater movement of capital, technology, and talent by establishing cross-regional industrial parks or economic cooperation zones. This is in line with Peng Wenying’s and other researchers’ conclusions that land green use efficiency is significantly improved by economic development [21]. At the level of industrial synergy, the government needs to formulate more targeted industrial policies to guide industrial upgrading and transformation. For traditional industries, financial subsidies, tax incentives, and other policies can be used to encourage enterprises to carry out green technological transformation and develop in the direction of green industries [38]. In line with the results of this paper, Wang Yongqing and other researchers from Northeast Forestry University examined the temporal and spatial variations in land use efficiency as well as the factors that influence it in China at the county level. Their findings demonstrated that upgrading industrial structures can significantly increase land use efficiency [39]. In terms of opening up to the outside world, international cooperation channels should be actively expanded, not only to introduce technology and capital but also to focus on the introduction of international advanced concepts of land resource management and environmental standards system, combining international standards with the actual situation in Beijing, Tianjin, and Hebei, and creating standards for managing the environment and utilization of land that are suitable for the local area. Researchers like Su Qiangjun of Chang’an University found that economic development and opening up to the outside world have a positive effect on the improvement of land use effectiveness in an investigation on the spatiotemporal pattern of land green use efficiency and its influencing factors in cities in the middle and lower reaches of the Yellow River. This finding is consistent with the findings of this study [40].

The negative impact of population urbanization and agglomeration on the efficiency of green land use in the Beijing–Tianjin–Hebei region urgently requires us to re-examine the urbanization development model. On the one hand, compact development should be emphasized in urban planning to raise the level of intensification of land usage. For example, the development of a public transportation-oriented development (TOD) mode, with high-density and multi-functional land development around public transportation stations, reduces the encroachment of urban sprawl on ecological land [41,42,43,44,45]. On the other hand, there is a need to strengthen environmental management and ecological restoration in areas of population concentration. Increasing investment in urban sewage treatment, garbage disposal, and other infrastructure would improve the carrying capacity of the environment. At the same time, ecological restoration projects should be implemented to restore the ecological functions of ecological land damaged by population concentration, such as wetlands and green areas around cities. Our study’s findings are consistent with those of Guan Zhe and other researchers who examined the spatial and temporal evolution of land use efficiency and its influencing factors in urban agglomerations. They found that urban economic growth positively affects land use efficiency, while population density negatively affects it [19]. In the study “The influence and spatial effect of new urbanization and urban land green use efficiency in the Yangtze River Delta region”, Li Jianbao and other researchers discovered that new urbanization has a positive spatial spillover effect on land green use efficiency, with noticeable spatial variations. This finding is slightly different from that of the current study, which shows how different regions’ urbanization affects efficiency in different ways [46].

The efficiency of green land use is strongly connected with ecological output and environmental control, underscoring the need to enhance ecological efficiency and environmental management [47]. Strengthening law enforcement against environmental infractions during land use and further improving the regulatory framework are essential for environmental control. Geographic information systems and satellite remote sensing are examples of contemporary information technology that are used to track land use and development in real time, identify infractions, and promptly address them. For the enhancement of the level of ecological output, the development of green industrial models such as eco-agriculture and eco-industry should be encouraged. To raise the ecological added value of agricultural products and decrease the use of chemical fertilizers and pesticides, ecological planting and breeding methods should be encouraged in the agricultural sector. In the industrial sector, eco-industrial parks should be set up to realize resource recycling among enterprises and increase the level of ecological output. Ji Zhiheng and other scholars studied the spatial differences in land use efficiency and the driving mechanism based on 285 prefecture-level cities in China and found that under environmental constraints, industrial structure upgrading has a significant positive contribution to urban land use efficiency, which is consistent with the conclusions of this study [48]. The Yangtze River Delta area is the research object used by Jin Xiaobin and other Nanjing University scholars to investigate the spatial effect of urban–rural integration on the influence of low-carbon land use efficiency. Their findings indicate that urban–rural integration is a significant factor influencing low-carbon land use and that its degree of development favorably encourages the use of land that is low-carbon, while science and technology inputs and opening up to the outside world also have a positive effect [49].

Building more green space should be a key component of land resource management since it has a variety of beneficial effects on the effectiveness of green land use in Beijing, Tianjin, and Hebei. In urban planning, a sufficient proportion of green space should be ensured, and the green space system should be rationally laid out. For example, an urban greenway network should be constructed to connect parks, green areas, and nature reserves to form continuous ecological corridors and improve the ecological connectivity of green areas. At the same time, the management and maintenance of green areas should be strengthened to improve their quality. Urban forest construction should be carried out, and native tree species suitable for local climate and soil conditions should be selected to improve the ecological service function of green areas. In addition, it is possible to explore the combination of green space construction with eco-tourism, leisure, and recreation industries to achieve a win–win situation in terms of ecological and economic benefits and to further enhance the efficiency of green land use. Lu Xinhai and other scholars from Central China Normal University, in their research on the driving factors of land green use efficiency in Chinese cities, found that the spatial heterogeneity of the factors is strong, such as the fact that ecological resources mainly have an impact on North China, the level of economic development and urban spatial agglomeration is mainly affecting Southwest China significantly, and population rush and land finance dominate the Northeast. According to this result, Lu Xinhai and other scholars suggested that in the future, city-specific policies should be implemented to achieve differentiated measures to enhance the efficiency of green urban land use [50].

In conclusion, the improvement of land green utilization efficiency in the Beijing–Tianjin–Hebei region is a complex systematic project that requires careful consideration of environmental, societal, and economic aspects, among others. Through the implementation of the above strategies, it is expected to realize the sustainable use of land resources and the improvement of the ecological environment in the study area and establish a strong basis for the region’s sustained steady growth. Research afterward should further quantify the degree of influence of different strategies on the efficiency of green land use, explore new models of synergistic development, etc. This study has some limitations in data acquisition and indicator system construction, failing to refine to the county or township level. Thus, future studies ought to concentrate more on analyzing the green utilization characteristics of different regions in depth from a more detailed spatial scale to achieve more precise policy guidance and land governance. At the same time, it is suggested that more comprehensive social, economic, and ecological benefits should be considered in the evaluation of green utilization efficiency, and the optimal combination of inputs and outputs should be explored to encourage an environmentally friendly expansion of green land use.

5. Conclusions

From an input–output standpoint, this study developed an index method for evaluating land green use efficiency in the Beijing, Tianjin, and Hebei region. The super-efficiency SBM model and ML index were used to examine the changes in land green usage efficiency and total factor production in the Beijing–Tianjin–Hebei region between 2010 and 2022, based on statistical yearbook data. Meanwhile, within the framework of coordinated development in Beijing, Tianjin, and Hebei, the Tobit model is used to extensively analyze the driving mechanism of the change in land green utilization efficiency. The following are the main conclusions:

First, from the standpoint of the pattern of geographical dispersion, the green utilization efficiency of land in the Beijing–Tianjin–Hebei region shows the characteristics of “high in the middle and low around”, and the imbalance is more significant. The green development capacity of some low-efficiency regions needs to be strengthened, and the radiation capacity and influence range of high-efficiency regions need to be further improved to drive the region’s coordinated development. Beijing has long held the top spot in the Beijing–Tianjin–Hebei region for land green use efficiency, demonstrating the benefits of high-tech and contemporary service sectors for effective land resource use. In contrast, because of their traditional industrial structure and environmental pollution issues, several cities in Hebei Province, such as Shijiazhuang and Handan, have lower levels of efficiency.

Second, from the standpoint of evolving trends, Beijing’s land green utilization efficiency has always been at a high level, and although it slightly decreased in 2022, it is still ahead of other cities, thanks to its industrial structure dominated by high-tech and modern service industries, which has the innate advantage of sufficient capital and technology to invest in land green development. Tianjin is more stable overall, with efficiency values fluctuating around 1.0, which is related to its slow industrial transformation and upgrading as a traditional industrial and port city. The situation of prefecture-level cities in Hebei Province varies. Cangzhou, Langfang, and Tangshan, which are close to Beijing and Tianjin, have taken advantage of the opportunity for non-capital function relocation, and with the good industrial foundation of each city, they have efficiently undertaken the transfer of industries from Beijing and significantly increased the per capita economic output. At the same time, they have strengthened ecological construction and environmental governance, and the efficiency of the green utilization of land has performed relatively well. Langfang stands out among them for enhancing the effectiveness of land green usage and has achieved good results in industrial upgrading and ecological environment construction in the process of undertaking the relocation of Beijing’s non-capital functions. Baoding, Xingtai, Handan, and Qinhuangdao have lower efficiency and little change, mainly affected by resource consumption and environmental pollution of traditional industries, and the green utilization of land is greatly restricted. Zhangjiakou City, Chengde City, and Hengshui City are relatively stable and mostly in the effective state, able to better balance ecological protection and economic development. Shijiazhuang City had a rising trend before 2015 due to the rapid economic development brought about by the large-scale input of land resources, resulting in an increase in environmental pollution and other non-desired outputs. In Shijiazhuang City, the land’s average industrial sulfur dioxide was as high as 8.703, and in the region as a whole, the green use of land was less efficient. However, with the implementation of the Beijing–Tianjin–Hebei coordinated development plan and the promotion of ecological civilization construction strategies, land green use efficiency was greatly improved during the process of increasing desired outputs and decreasing non-desired outputs. In 2015, the average per capita industrial wastewater was reduced by 5.54% compared to 2010, and the green coverage rate of built-up areas increased by almost 4% between 2015 and 2019. All things considered, the Beijing–Tianjin–Hebei region’s land green utilization efficiency is strongly tied to the local industrial structure, economic development mode, ecological environment construction, and regional synergistic development. The Beijing–Tianjin–Hebei synergistic development strategy has improved the synergistic nature of each location, which helps to increase the land green utilization efficiency.

Lastly, when the driving mechanism of land green utilization efficiency is examined within the framework of the region’s synchronized progress, it is discovered that elements like ecological output, industrial structure, environmental control, and economic development level all contribute positively to land green utilization efficiency. Therefore, the benefits of industrial synergies, economic synergies, and opening up to the outside world should be effectively leveraged in the practice of land resource management in the Beijing–Tianjin–Hebei region, and environmental control should be enhanced. Simultaneously, the negative issues brought about by population concentration and urbanization should be appropriately addressed, and the creation of green space should be given top priority in order to achieve sustainable city development by enhancing the effectiveness of green land use through all-encompassing measures.

The research presented here contributes to the theoretical framework of regional land green use efficiency, which is theoretically significant. In addition to providing a new empirical basis for the concept of imbalance in regional economic development in the land use domain, the analysis of geographical disparities improves the characteristics of the rapid growth of green efficient land use in different regions. By examining the influencing elements, the multi-factor mechanism of land green usage efficiency is improved, the interplay between economic, industrial, and environmental aspects is revealed, and the theoretical expansion that follows is supported.

In reality, this study supports regional planning and policymaking to a certain degree and gives the government a precise foundation on which to formulate policies for various efficiency regions. For example, it encourages high-efficiency regions like Beijing and Langfang to play the role of radiation-driven, while low-efficiency regions like Baoding and Xingtai are encouraged to increase ecological management inputs and promote industrial transformation. By enhancing environmental control, optimizing the industrial structure, and increasing investment in ecological construction based on the influencing factors, managers can achieve the sustainable and efficient use of land resources in the region and support the region’s overall sustainable development.

In conclusion, in order to attain the region’s goal for environmentally friendly growth, expanding the optimization of economic and industrial structure, strengthening technological innovation and policy coordination, and striking a balance between economic development and environmental protection are very necessary steps in the process of improving the land green use efficiency in the region. By providing a solid theoretical basis and decision-making reference for the practice of land resource management and related policy creation in the Beijing–Tianjin–Hebei region, this study’s findings are expected to favorably impact future research and practice.