Abstract

As ‘the lungs of the Earth,' forests are generally accepted as a major force affecting the terrestrial ecological balance. Forestry is not only one of the primary industries in the national economy but also an important part of public welfare undertakings. Therefore, forestry development plays a significant role in supporting the development of the social economy and conservation culture. China has stepped into a critical period of switching, accelerating, upgrading, and transforming forestry development in the context that China has ceased commercial logging of natural forests and set the ecological conservation redline. Hence, understanding and improving forestry productivity is not only a general requirement of sustainable forestry operation but also a practical issue associated with the development of human society.

Researchers at home and abroad made clear and accurate elaborations on the concepts, measurement methods and influencing factors of forestry productivity, whereas little attention was paid to the interactive relations between forestry productivity and regional economic development. Mynarski et al. (2018) provide an overview of forest management efficiency assessment, giving the definition of economic entity efficiency and productivity and the categories of efficiency assessment methods (ratio analysis, parametric and nonparametric methods to measure forest efficiency; Diaz-Balteiro and Romero 2008, Mynarski 2018). Šporčić et al. (2009) assessed the efficiency of basic organizational units in the forestry goals and forest offices by applying Data Envelopment Analysis (DEA). In a study by Zhang and Kang (2017), the super efficiency DEA–Tobit model was used to identify the factors influencing China's forestry productivity based on the data collected from 30 provinces (cities and districts) during 2000 to 2014. When studying the influence of labor transfer and cooperative operation on household-based forestry productivity, Han et al. (2018) found that labor transfer had an inhibitory effect on forestry productivity, while forestry cooperative business showed the reverse. Kovalčík (2018) used DEA to assess forestry efficiency in European countries during 2005 to 2008. The results show that the average efficiency of forestry in Slovakia is lower than average compared with other European countries, and the efficiency varies greatly from country to country (Kovalčík 2018). Based on the sample data of 364 farmers, the influence of the reform policy of the collective-owned forest tenure system on farmers' forestry productivity was investigated by Yang et al. (2019), the results of which indicated that policies such as forestry insurance, forestry cooperatives, and forestry sci-tech services, played a positive role. The DEA–Malmquist Index and panel Tobit model were used by Cao and Wang (2019) to probe the regional difference and temporal variation in China's forestry productivity and the influence of fiscal support and forestry tenure reform on forestry productivity. Neykov et al. (2019) revealed the results of the efficiency of forestry and forest-based industries throughout the European Union and have concluded that Bulgaria is inefficient. Advanced manufacturing technology and investment have important influence on efficiency. Lundmark et al. (2020) found that improving the efficiency of the forestry sector will significantly affect our likelihood of achieving long-term sustainability and mitigation of climate change. By analyzing the coupling coordination between the economic development efficiencies and the ecological conservation levels of key state-owned forest zones using the coupling coordination degree model, Cao et al. (2017) concluded that the coupling degree between the economic development efficiency and the ecological conservation level of key state-owned forest zones was antagonistic and showed a moderately to highly coordinated coupling trend. Considering the significant difference in economic development level between Chinese provincial regions, it is difficult to determine whether current forestry productivity can satisfy the requirements of regional economic development without identifying the evolutionary law of China's forestry productivity and the situation of regional economic development, as well as creating a win–win situation for forestry conservation and sustainable economic development. To this end, this study aims to calculate the forestry productivities and the overall evaluation indexes of the regional economic development levels of various Chinese provincial regions and investigate the coupling coordination between forestry productivity and the regional economic development level using the coupling coordination degree model, thereby providing evidence for boosting the coordinated development of forestry productivity and the regional economy and offering scientific and targeted suggestions for the sustainable utilization of forest resources, as well as the sound and environmentally friendly development of the social economy.

Material and Methods

Methodology and index system establishment

Super efficiency slacks-based measure (SBM) model.—

Forestry productivity is measured using the super efficiency slacks-based measure (SBM) model in this study. Compared with traditional data envelopment analysis, the super efficiency SBM model considers the influence of loose variables, random errors, and the external environment on efficiency measurement and resolves the disadvantage that the comparison cannot be made when the efficiency value exceeds one. The super efficiency SBM model can be denoted as follows (Chang et al. 2014, Cheng 2014):

Entropy weight Mmethod.—

The comprehensive evaluation index of the regional economic development level is measured using the entropy weight method. Based on the original data information, this method assigns weight to each index according to the information each index contains and the correlation between indexes, which reduces the bias incurred by subjective factors during weighting. The calculation procedure of the entropy weight method is shown as follows (Shannon 1948, Tang et al. 2018):

Coupling coordination degree model.—

Coupling describes the overall effect generated by the interactions between inter- or intrasystem factors, which are stronger than those generated by a single system. Coordination is the concentrated reflection of the positive correlation formed based on such interactions, indicating a trend where the system evolves from disorder to order. Together, coupling coordination shows the correlation between forestry productivity and regional economic development level in a more comprehensive manner because it can help identify whether the coupling relationship between them is positive and sustainable. The higher the coupling coordination degree between forestry productivity and regional economic development level is, the better they can positively interact with each other and promote mutual development. The calculation procedure of this model is shown as follows (Li et al. 2012, Tang et al. 2018, Cui et al. 2019): where α and β are the coefficients of the comprehensive coordination index to be estimated and α + β = 1. In this study, we consider that forestry productivity and regional economy should develop coordinately, and the latter slightly outweighs the former. Therefore, α is set to 0.4 and β is set to 0.6, based on which the coupling coordination degree between forestry productivity and regional economy is graded (as shown in Table 1).

Table 1.— Grading of coupling coordination degree.

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GM (1,1).—

In this study, the coupling coordination degree between forestry productivity and regional economic development level is predicted using GM (1,1). Considering that it is unnecessary to consider the a priori characteristics of the original data in GM (1,1) buildings, GM (1,1) is random and can weaken the time series. With fewer data required, GM (1,1) prediction can be made as long as the data size exceeds four. Given space limitations, the modeling process and model accuracy standard are not covered here (Sheu et al. 2014, Javed and Liu 2018, Lu et al. 2020).

Index system establishment

Forestry productivity.—

Forestry productivity refers to the capacity of forestry production to convert various inputs into an output subject for the constraints of the external economic environment and internal management. It is the ratio of forestry input to forestry output that shows the optimization process of forestry production factor allocation from the perspective of output or input. According to the theory of economics, capital, labor, and land play a critical role in production input. Forestry output not only includes the economic benefit of forestry but also ecological and social benefits. The forestry productivity input index system of this study is built based on three factors: land, capital, and labor; the forestry productivity output index system is built on another three factors: economic, ecological, and social benefits (Table 2; Mlynarski and Predki 2017, Tian et al. 2017, Liu et al. 2018, Nikolay et al. 2018;).

Table 2.— Forestry productivity evaluation index system.

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Regional economic development level.—

The regional economy is the production complex created by the interactions between internal and external economic development factors within a specific region. The rationality of index selection and data availability are comprehensively considered based on a literature review. In this study, the regional economic development-level index system is built based on three factors: the scale, structure, and potential of the economy. State fiscal revenue per capita = state fiscal revenue / the total regional population; social fixed asset investment per capita = total social fixed asset investment / total regional population; education expenditure per capita = education expenditure / total regional population; and sci-tech expenditure per capita = sci-tech expenditure / total regional population (Table 3; Li and Cui 2018 and Yao and Men 2020). In particular, education expenditure and sci-tech expenditure are the expenditure items in the general public budget.

Table 3.— Regional economic development level evaluation index system. GDP is gross domestic product.

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Results and Discussion

Forestry productivity and regional economic development coupling coordination means boosting forestry productivity and economic development level, narrowing the relative gap, and contributing to a sound and orderly coupling system of forestry productivity–regional economic development based on the actual situations of various regions and objective laws. Regional economic development is the foundation for improving forestry production factors. Forestry development requires support from a variety of resources, including policy, capital, workforce, and technology, all of which depend on regional economic development. The regional economy plays an important role in increasing forestry factor input, stimulating forestry output, and ultimately improving forestry production factors. Regional economic growth is subject to the influence of forestry productivity. Forestry production factor improvement means concentrated, efficient, and intensive utilization of land, capital, and labor. Moreover, the effective allocation of forestry production factors, such as land, labor, and capital, can boost industrial structure optimization, which further promotes regional economic growth.

Measurement of forestry productivity and regional economic development level

Forestry productivity measurement—

The super efficiency SBM model is used to measure the forestry productivities of 31 Chinese provincial regions (provinces, municipalities, and autonomous regions) during 2009 to 2018, and the calculation results are shown in Table 4. During this period, China's forestry productivity was <1 on average, ranging between 0.502 and 0.587. This evidence indicates that both the input and output factors of forestry production were inefficiently allocated, which led to a low overall forestry productivity and a large gap between the actual output and the optimal output at the current input level. Thus, there is still plenty of room for forestry output improvement. This result coincides with the research findings obtained by Luo et al. (2017) and Wei (2016). The forestry productivities of eastern, central, and western China were far from efficient during that decade. Although the forestry productivities of most provincial regions showed a rising tendency, they differed among provinces. Specifically, the forestry productivities of the Tianjin, Jiangsu, Zhejiang, Guangdong, Hainan, and Sichuan Provinces and the Tibet Autonomous Region (Tibet) reached and maintained an efficient state since 2009. Of all the regions, Shanghai enjoyed the highest forestry productivity. The forestry productivities of the Fujian, Shandong, and Yunnan Provinces were improving year by year, developing from an inefficient level to an efficient level; the forestry productivities of the Hebei, Jilin, and Hunan Provinces; Inner Mongolia Autonomous Region (Inner Mongolia); and Xinjiang Uygur Autonomous Region (Xinjiang) fluctuated and dropped slightly. A fluctuating upward trend of forestry productivity observed in the rest of the provinces, but none of them had reached an efficient state.

Table 4.— Measurement results of forestry productivity during 2009 to 2018.

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Measurement of regional economic development levels.—

The comprehensive indexes of regional economic development levels of 31 Chinese provincial regions during 2009 to 2018 are measured using the entropy weight method, and the calculation results are shown in Table 5. The Chinese provincial economies were not only significantly different from each other in terms of development level, but they were also concentrated, wherein eastern coastal regions—such as the Guangdong Province, Jiangsu Province, Shandong Province, and Shanghai—enjoyed high levels of economic development, and regions in central and western China—such as the Qinghai Province, Tibet, Jilin Province, Liaoning Province, and Heilongjiang Province—showed low levels of economic development. This finding suggests that strengthening economic cooperation between eastern, central, and western China to narrow the interregional economic development gap is still a major issue demanding a prompt solution.

Table 5.— Comprehensive indexes of regional economic development level during 2009 to 2018.

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Analysis of the coupling coordination degree between forestry productivity and regional economic development

The coupling coordination degree model is used to measure the coupling coordination between the forestry productivities and the regional economic development levels of 31 Chinese provincial regions during 2009 to 2018, based on which their development stages and trends are identified, and the calculation results are shown in Table 6. In general, the coupling coordination degree between the forestry productivity and economic development level in China rose steadily at a slow rate from 0.458 to 0.532, suggesting that it developed only from the minimally imbalanced stage to the barely coordinated stage. From a regional perspective, eastern China enjoyed the highest coupling coordination degree, followed by western China and central China. Eastern China was in the primarily coordinated stage; although its coupling coordination degree rose slightly, it had not reached the moderately coordinated stage. Central China was in the mildly imbalanced stage; its coupling coordination degree also improved slightly, but it had not reached the minimally imbalanced stage. Western China developed from the mildly imbalanced stage to the minimally imbalanced stage. The reasons for this phenomenon are listed as follows: (1) Eastern China performed better in economic development and conservation culture because of geographical and policy advantages, which contributed to a higher coupling coordination degree. (2) Hampered by the slow forestry transformation and upgrading, central China failed to efficiently transform the resource-dependent traditional economic development pattern, which had led to a lower coupling coordination degree than those of eastern and western China. (3) The Chinese central government introduced adequate ecological policies and provided sufficient financial support for western China over the past few years. Together with technological advancement, these factors enabled western China to boost forestry productivity, improve regional economic development, and ultimately promote a higher degree of coupling coordination.

Table 6.— Measurement results of coupling coordination degree during 2009 to 2018.

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From a temporal perspective, the coupling coordination degrees between the forestry productivities and the regional economic development levels of 31 Chinese provincial regions during 2009 to 2018 could be divided into three stages: fluctuating upward stage, fluctuating downward stage, and relatively stable stage. The coupling coordination degrees between forestry productivity and regional economic development level ranged between 0.2 and 1. Specifically, the Beijing, Anhui, Fujian, Shandong, Jiangxi, Henan, Hubei, and Guangdong Provinces; the Guangxi Zhuang Autonomous Region (Guangxi), Hainan Province, Chongqing, Guizhou Province, and Yunnan Province; and Ningxia and Xinjiang were in the fluctuating upward stage; the Hebei, Shanxi, Jiangsu, Zhejiang, Hunan, Sichuan, Qinghai, Shaanxi, and Gansu Provinces; and Tianjin, Shanghai, and Tibet were in the relatively stable stage; and Inner Mongolia, the Liaoning Province, Jilin Province, and Heilongjiang Province were in the fluctuating downward stage. The coupling coordination between the forestry productivity and the regional economic development level of each provincial region showed either an upward or downward trend, evidencing that there were multiple labile factors in regional economic development–forestry development mutual promotion that called for scientific and systematic promotion of coordinated development between forestry and regional economy based on local resources, environment, and other relevant factors. Although the overall coupling coordination degree between forestry productivity and the regional economic development level of China was less than ideal, it continued to progress.

From a spatial perspective, Chinese provincial regions featured low degrees of coupling coordination between forestry productivity and regional economic development and differed significantly. Spatially, the overall coupling coordination between the forestry productivity and regional development level of China was high in the east and south and low in the west and north. By 2018, Shanghai and Tianjin enjoyed the highest coupling coordination degrees and were in the highly coordinated stage and moderately coordinated stage, respectively. Qinghai showed the lowest coupling coordination degree and was in the moderately imbalanced stage. A majority of the provincial regions were in the mildly imbalanced stage, the minimally imbalanced stage, the barely coordinated stage, or the primarily coordinated stage: Hebei, Liaoning, Jilin, Henan, Hunan, Guizhou, and Shaanxi Provinces, and Ningxia and Guangxi and Xinjiang; the Chongqing, Anhui, Jiangxi, Hubei, Sichuan, and Yunnan Provinces; the Beijing, Fujian, Shandong, and Hainan Provinces; and the Tibet, Jiangsu, Zhejiang, and Guangdong Provinces. The coupling coordination between the forestry productivities and the regional economic development levels of Shanghai, Tianjin, the Jiangsu Province, and Zhejiang Province had been maintained at a high level, which was closely associated with a profound local economic basis, substantial workforce support, high sci-tech levels, and macro policies. Although Inner Mongolia, and the Heilongjiang, Jilin, and Liaoning Provinces are famous for abundant forestry resources, the extensive forestry development pattern they followed resulted in low forestry productivity, which together with unsatisfactory economic development confined their coupling coordination to the moderately and mildly imbalanced stages.

Prediction for the coupling coordination degree between forestry productivity and regional economic development

GM (1,1) is used to predict the trend of coupling coordination between forestry productivity and regional economy over the next 5 years based on the coupling coordination degrees between the forestry productivities and the regional economic levels of Chinese provincial regions during 2014 to 2018 obtained above, thereby creating more targeted and practical policy suggestions. As shown in Table 7, P = 1, C = 0.267 and the mean relative error is 1.194 percent, suggesting that the prediction result satisfies the modeling requirements.

Table 7.— GM (1,1) predictions for 2019 to 2023.

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Overall, the coupling coordination degree between forestry productivity and the regional economic development in China exhibits a stable and slowly rising trend. The coupling coordination degree between forestry productivity and the regional economic development in China will still remain in the barely coordinated stage in the next 5 years. Eastern and western China will be in the primarily coordinated stage and the minimally imbalanced stage, respectively; central China may shift from the mildly imbalanced stage to the minimally imbalanced stage. Specifically, the Hebei, Anhui, Fujian, Henan, Guangdong, Sichuan, Guizhou, Yunnan, and Gansu Provinces are the provincial regions that will progress in coupling coordination, of which the Yunnan Province enjoys the fastest growth and may leap from the minimally imbalanced stage to the primarily coordinated stage. The Liaoning Province, Shandong Province, Qinghai Province, Ningxia, and Xinjiang are the provincial regions that will retrogress. The rest of the provincial regions will remain unchanged. These results suggest that the overall coupling coordination degree between forestry productivity and the regional economic development of China will improve modestly in the next 5 years, but at a low rate; there are even some provincial regions that will retrogress. Therefore, there is still a long way to go to realize mutual promotion and coordinated development between forestry productivity and regional economic development levels in all provincial regions of China.

Conclusion and Suggestions

In this study, the coupling relationship between the forestry productivities and the regional economic development levels of 31 Chinese provincial regions during 2009 to 2018 and its development trend are analyzed and predicted using the coupling coordination degree model based on the coupling characteristics of forestry productivity and regional economic development level from both spatial and temporal perspectives. Research findings suggest that the coupling coordination between forestry productivity and the regional economic development of China is mostly in the minimally and mildly imbalanced stages; although the coupling coordination between the forestry productivity and the regional economic development level of each provincial region showed either an upward or downward trend, it maintains a positive momentum; and spatially, the overall coupling coordination between the forestry productivity and regional development level of China was high in the east and south and low in the west and north.

Based on the above analysis, the following suggestions for boosting positive and coordinated interactions between forestry productivity and regional economic development are proposed: Firstly, it is crucial to create forestry economic development plans for various forest zones to develop forest resources that are in line with local conditions with a prerequisite of sustainable development. The concrete measures include regulating factor input allocation based on marginal contributions from various production factors; combining governmental fiscal investment and social investment and perfecting the forestry fund supervision mechanism; establishing and perfecting a sci-tech support system to improve the quality of forestry practitioners; improving forestry supporting facilities and creating a natural environment appropriate for forest growth. Secondly, confronted with the new situation, challenges, and opportunities that the new normal brings about, it is advisable to strengthen investment and support for emerging industries and actively promote structural reform; increase investment in general education and science education by introducing science education programs and hastening the construction of competitive scientific workforce training institutions; and encourage developed regions to help underdeveloped regions to ensure that they develop simultaneously in a normalized, scientific, and institutionalized way. In addition, interregional in-depth cooperation should take place to share quality resources, complement each other's advantages, and ultimately improve their respective economic levels. Moreover, governments are supposed to serve roles in policy guidance and fiscal support to create a stable environment that ensures coordinated development between forestry and the regional economy. They should not only give full play to the boosting effect of forestry productivity on regional economic development, but also effectively improve the regional economy's support and resource allocation for forestry productivity in the environment, education, infrastructure, and workforce.