Interactive relationships between industrial,urban,agricultural,information,and green development

Abstract

Achieving the green development, industrial development, urban development, agricultural development, and information development (five developments) has become a current modernization strategy in China. To promote the five developments from an integrative perspective, this study first analyzed the interactive relationships between the five developments, then a panel vector autoregression model and impulse response functions were applied to evaluate the dynamic impacts between the five developments. The empirical results show that (1) The delivery times of the impacts between each development were approximately one year and all the impacts decreased over time. Many impacts disappeared after the fourth year, whereas the impact of urban development on itself were longer lasting; (2) For each development, its current level is a dominant factor for the future development. Meanwhile, the promoting impacts from other developments were also essential, where their strength and trend were different; (3) There were certain conflicts between the five developments, especially for the relationships between the green development and other developments.

Keywords

Green development five developments interactive relationships dynamic impacts panel vector autoregression impulse response function

Introduction

The development pattern with high energy consumption in China restricts the sustainable development. In 2015, the Chinese government promised to the United Nations that China’s intensity of CO₂ emissions will be 60–65% lower in 2030 than that in 2005.¹ Under this background, in March 2015, the Chinese central government issued an important document, namely, “Opinions on Accelerating the Construction of Ecological Civilization” which has focused on the green development when promoting industrial development, agricultural development, urban development, and information development. Therefore, coordinately promoting the green development, industrial development, agricultural development, urban development, and information development (five developments) has become the latest modernization strategy in China.²

Industrial development is a process in which the traditional agricultural society has transformed into a modern industrial society. The nonagricultural industries has become the dominant components of the economy and the ratio of value added of nonagricultural industries to the gross domestic product (GDP) remains increasing; urban development is a process in which the urban areas and urban functions are continuously expanding. The rural population continues to transfer from rural areas to urban areas, and the proportion of urban population has kept rising; agricultural development is a process in which agricultural sector follows the laws of market economy, applies advanced agricultural technologies, equipments, infrastructure, and adopts effective management and thus significantly increases agricultural productivity as well as the added value; information development involves the growing application of information technologies and equipments, expansion of the information industries, and the construction of information infrastructure; green development is the process that continuously reduces resource consumption and environmental risks while promoting economic growth.

As parts of modernization, five developments are interconnected, mutually promoted, or mutually restricted. Hence, promoting any of these developments has to consider the relationships with other developments. The whole modernization will be hindered if there are significant conflicts between the five developments.³ Therefore, studying the relationships between the five developments as well as finding out the shortboard of the five developments are of great significance;⁴ and this provides the basis for the modernization decision, helps to strengthen the promotion and reduces the inhibition between the five developments, and thus increases the level of whole modernization.⁵

The studies related to the five developments can be summarized into three parts. The first part focused on the industrial development, urban development, and agricultural development. The second part added the information development since the information development is more and more critical to the economic development. The third part emphasizes green development due to the growing constraints of natural resources and environment.

(i) Industrial development, urban development, and agricultural development. As for the impacts of agricultural development on the industrial and urban development, many studies illuminated the interactive mechanism between agriculture and industry⁶ or between agriculture and urbanization.⁷ Agricultural development needs industrial sector to provide its production resources.⁸ Xia and Liu⁹ agrued that the agricultural development has a negative effect on the industrial development in the long run. Additionally, the rural labor that transfer from rural areas to urban areas drives urban development, making traditional agricultural society turn into modern society.^10,11 In terms of the effects of the industrial/urban development on agricultural development, Long et al.¹² claimed that urbanization and industrialization play a positive role in agricultural development. Certain researches studied the relationship between industrialization and urbanization.¹³ Murakami¹⁴ believed that industrialization promotes the urbanization.

(ii) Information development. With respect to industrial development, many studies have noted that the construction of information infrastructure contributed to the development of enterprises and industries.^15,16 The information development can reduce the transaction costs and improve output efficiency and scale.¹⁷ Regarding urban development, some scholars explained the promoting effect of information technologies on the urban economic development and they observed that the changes in information technologies directly affect the urbanization.^18,19 Graham and Simon²⁰ summarized the effects of information development on urban development. These effects include the aspects of collaborative, derivative, substitution, and reinforcement. As for agricultural development, Kauffman and Kumar²¹ proposed that grain productivity is associated with the level of information technology.

(iii) Green development. In terms of industrial development, the environment can be affected by industrial activities.²² Newell et al.²³ noted that optimization of industrial structure plays an active role in reducing energy intensity; energy elements flow from inefficient industries to highly efficient industries with industrialization. Additionally, investment in technological innovation probably increases with the industrial development; and industrial technological progress can decrease energy consumption intensity.^24,25 Regarding urban development, there is a positive correlation between urban development and energy consumption.^26,27 The growing vehicles²⁸ and the rising demand for products²⁹ in urban areas have substantially increased energy consumption. Poumanyvong and Kaneko³⁰ pointed out that the adverse effect of urban development on energy use is heterogeneous; the negative effect is more significant in high-income regions than that in low-income regions. As for agricultural development, agricultural modernization increases the environmental impacts³¹ and the energy consumption. Fluck³² indicated that agricultural development increases direct and indirect energy consumption. Ghisellini et al.³³ conducted an empirical research and found that the agricultural energy intensity in Netherlands and Spain is higher than that before. With respect to information development, the energy consumption can increase considerably despite the upgrade of the information and communication technology (ICT).³⁴ The direct impact of information technologies on energy consumption is that people continue to consume information products and use Internet services, and the use scope keeps extending. Hence, the consumption intensity of electricity is growing remarkably.³⁵ Many studies had also confirmed that ICT increases energy intensity; the increased use of television, computer, and hardware, etc. have the negative impacts on power consumption.^36–38

As mentioned above, a range of studies have analyzed the relationships between two or three developments (e.g. mutual/unilateral promotion, mutual/unilateral inhibition, or uncertain influence caused by changing external conditions), providing the important referencing values.^{9,13,27,36,39} However, three gaps still need to be filled. (i) Although green development, industrial development, urban development, agricultural development, and information development have become an integrative modernization strategy in China since 2015, few empirical studies have evaluated their relationships from this integrative perspective. Due to systematic complexity, the relationships between two developments may change when they are in the whole system that includes all developments. To better promote the modernization, it is necessary to evaluate these effects under an integrative framework. (ii) Many studies center on the static impacts,^30,40,41 whereas little studies focused on dynamic impacts, especially under the aforementioned integrative perspective. Therefore, we need to provide more studies on how the impacts between the five developments change over time. (iii) Because macro factors usually have two-way impacts (e.g. urban development and industrial development affect each other), the endogenous problem may exist when using the traditional regression models such ordinary least squares or fixed-effect model; this results in a bias estimation of coefficients.^42,43 Therefore, an appropriate model is needed to study the effects between these macro factors. To fill the above gaps, this study integrates green development, industrial development, urban development, agricultural development, and information development into a unified framework. Meanwhile, a panel vector autoregression (PVAR) model is established based on the mechanism analysis to solve the endogenous problem and small sample problem. Afterward, the impulse response function (IRF) is used to study the dynamic impacts between the five developments over time.

The remainder of the study is organized as follows: The next section describes the theoretical relationships. “Methodology” section details the model and variables. “Analysis of empirical results” section discusses the empirical results, and the final section summarizes the major conclusions and recommendations.

Analysis of the interactive relationships between the five developments

Impacts on industrial development

As for the impacts of urban development on industrial development, urban development makes a substantial number of people shift from rural areas to urban areas; this provides abundant labor to support industrial development. In addtion, urban development leads to talent centralization, which improves the quality of labor in the industrial sector. Moreover, infrastructure in urban areas is well developed than that in rural areas, providing better support for industrial production. Meanwhile, the industrial agglomeration in urban areas generates the economies of scale. In addition, urban development increases residents’ income, thus stimulating the demand of industrial products and promoting the industrial development.

In terms of the impacts of information development on industrial development, information development promotes the automation and intelligence in the industrial production and thus increases industrial productivity. Moreover, the development of information technologies and equipments such as multimedia technologies, communication technologies, and intelligent computers can trigger the production of information industries and thus promote industrial development.

Regarding the impacts of agricultural development on industrial development, according to the theory of production value, land, capital, and labor are irreplaceable elements in most production, and these elements are both critical in the industrial production and agricultural production. Because of this, the quick agricultural development results in rising production costs and growing demand of production elements such as of labor, capital, land in the nonagricultural sector, and thus creates a “competitive effect” which restricts the industrial development.

With respect to the impacts of green development on industrial development, green development policy stimulates the development of emerging industries such as environmental protection industries, high-tech industries, new energy industries, and service industries; these industries usually have high added value and low energy consumption. Moreover, according to the “innovation offsets”, the green revolution promotes the technological progress and speed up the upgrade of production equipments. Therefore, green development will increase industrial production efficiency and quality.

Impacts on urban development

In terms of the impacts of industrial development on urban development, industrial development requires considerable labor, making people transfer from rural areas to urban areas and thus promoting the urban development. Moreover, industrial development leads to the centralization of industries in urban areas and increases the industrial output value as well as the government revenue; these changes all promote the urban development.

As for the impacts of agricultural development on urban development, agricultural development improves the agricultural productivity, which enables more farmers to move from the agricultural sector to the nonagricultural sector in urban areas and promotes urban development. Moreover, agricultural development requires considerable industrial products such as agricultural machines and chemical fertilizers. This growing demand leads to the development of associated industries in urban areas.

Regarding the impacts of information development on urban development, information technologies accelerate the flow of goods, labor, capital, and technologies in the urban area, thereby improving the urban association, resource management, and urban construction. Moreover, the technologies and information industries significantly contribute to the development of urban-related industries and infrastructure. Graham and Simon²⁰ have summarized the effects of informantion development on urban development as collaborative effect, derivative effect, substitution effect, and enhancement effect.

With respect to the impacts of green development on urban development, green development emphsizes energy conservation and emission reduction, probably restricting the extensive implementation of urban projects such as infrastructure construction, real estate projects, etc., which have high energy consumption, and restricting the development of high energy intensive industries and traffic systems. Therefore, green development, to a certain extent, slow down the rapid urban development based on high resource consumption.

Impacts on agricultural development

Regarding the impacts of industrial development on agricultural development, industrial development drives rural surplus labor to flow to cities; this change is helpful for the large-scale production and centralized agricultural management, and thus improves the production efficiency and economic efficiency in the agricultural sector. Moreover, industrial development can provide advanced materials such as agricultural machines and chemical fertilizers to the agricultural sector. Moreover, the technological progress caused by industrial development changes the traditional agriculture, improving its productivity. Additionally, the industrial development can provide financial support for agricultural development.

As for the impacts of urban development on agricultural development, urban development generates a vast market for potential consumers, enhances the supply of agricultural products and thus increases agricultural output and economic benefits. Moreover, urban development has absorbed a great deal of rural population, increasing the proportion of large-scale production and the centralized management of rural land, thus improving agricultural productivity.

In terms of the impacts of information development on agricultural development, information technologies support the intelligent management in the agricultural sector and thus improve production efficiency. Moreover, the networked information platform helps improve the control of agricultural production and the application of technologies such as planting and breeding technologies, production, storage, transportation technologies, prevention, and monitoring technologies, thus increasing the agricultural productivity.

With respect to the impacts of green development on agricultural development, the use of agricultural facilities and machines can be significantly increased with the agricultural development; this results in the increased energy consumption, whereas green development that requires energy saving probably restricts the use of some facilities and machines with high energy consumption, and thus slows down the agriculture development.

Impacts on information development

In terms of the impacts of industrial development on information development, industrial development provides production condition for information technologies. Moreover, industrial development increases the demand for information technologies and the applications of information technologies in the industrial sector.

With respect to the impacts of urban development on information development, the life pattern and consumption structure in urban areas increase the demand for information technologies, information products, and information facilities. Hence, urban development is a driving force for information development. Moreover, the urban areas have abundant labor, capital, and infrastructure, providing an appropriate environment for the development of the information industries and the information technologies.

Regarding the impacts of agricultural development on information development, information technologies and information equipments can be used in agricultural planting, breeding, production, storage, transport, and sales. Therefore, agricultural development, to some extent, improves the demand for information technologies and equipments, i.e. the agriculture development may promote the information development.

As for the impacts of green development on information development, the increased information products such as computer, television, and the growing scale and time of using Internet services both require as much energy as possible. Meanwhile, the operation of information equipments and infrastructure also require much energy (e.g. temperature and humidity control), whereas the green development may constraint the information development with high energy consumption and thus slow down the extensive information development.

Impacts on green development

With respect to the impacts of industrial development on green development, the output growth of the nonagricultural industry is higher than the growth of energy consumption. As a result, the development of nonagricultural industry reduces the energy intensity. Moreover, the growing high-tech industries, service industries, energy conservation and environmental protection industries with low consumption and high efficiency significantly decrease energy intensity and carbon emission intensity. Newell et al.²³ argued that the energy elements flow from inefficient industries to highly efficient industries with the industrial development.

As for the impacts of urban development on green development, urban development leads to a change in lifestyle and consumption structure, which increase the per capita energy consumption. Moreover, there are many high energy consumption issues in urban areas, e.g. the intensive construction of infrastructure and real estate, the cooling and heating system of buildings, the widespread use of vehicles—these issues result in the significant burden to the green development.

Regarding the impacts of agricultural development on green development, agricultural development that pursues high benefits is accompanied by using of a large number of machines, equipments, and infrastructure, which significantly increases the amount of energy consumption. Fluck³² asserted that there are direct and indirect energy consumptions caused by the agricultural development.

In terms of the impacts of information development on green development, information development enables people continue to use Internet services and information products such as television, computer, etc. Hence, the energy consumption is increasing substantially. Moreover, the construction and operation of information networks, the dissemination of information content, and the operation of information infrastructure such as the computer room and communication stations all significantly increases energy consumption. Meanwhile, to ensure the operation of information equipments, it is necessary to use air-conditioning and other high energy-consuming equipment to control the temperature and humidity in the room. Even worse, the information machines usually require an all-weather and uninterrupted operation, which further increases energy consumption.

Methodology

Data description

To conduct an empirical study, this study first collects the data to reflect the levels of the five developments. According to the concepts of the five developments (see “Introduction” section), (i) the level of industrial development is measured by the proportion of the nonagricultural added value to GDP, denoted as IND, where the original data is derived from “China Statistical Yearbook”; (ii) the level of the urban development is measured by the proportion of urban population to the total population, denoted as URB, where the original data is derived from “China Statistical Yearbook”; (iii) the level of the agricultural development is measured by the agricultural production value per unit land area, denoted as AGR, where original data is derived from “China Statistical Yearbook”; (iv) the level of the information development is measured by an information development index proposed by National Bureau of Statistics of China, which is denoted as INF, where the original data is derived from “China Information Yearbook”; (v) the level of the green development is measured by energy consumption per unit of GDP because many studies have noted that energy intensity or carbon emissions are the core indicators for the green development,^25,41 denoted as GRE, where the original data is derived from “China Energy Statistical Yearbook.”

Furthermore, to avoid the incomparability caused by price fluctuation between different years, all value variables, i.e. IND, AGR, and GRE, have been converted into 2005 prices by using the price indices. The data is currently available in an online database (https://doi.org/10.5281/zenodo.1283392) hosted by Zenodo.

Afterward, all data used for the VAR/PVAR model are required to be stable or have a long-term cointegration relationship. Hence, a unit root test for each data is conducted. Commonly tests include Levin, Lin, and Chu test; Im, Pesaran, and Shin test; Fisher-Augmented Dickey Fuller test; and Fisher–Phillips Perron test, where each test has a different assumption. As shown in Table 1, all data are stable after taking the first-order differential and logarithm. Therefore, the data can be used for VAR/PVAR model.

Table 1.

Test results of stability of variables.

Variables	LLC test	IPS test	ADF test	PP test
LnIND	−13.154***^a	−2.563***	94.152***	96.193***
LnURB	−3.801***	4.222	29.876	79.171**
LnAGR	−6.064***	1.017	59.927	65.480
LnINF	−4.032***	4.338	35.139	69.659
LnGRE	7.693	11.156	7.794	0.985
D(LnIND)	−14.425***	−7.159***	173.402***	222.891***
D(LnURB)	−10.400***	−4.282***	122.190***	152.051***
D(LnAGR)	−24.692***	−11.028***	225.754***	210.110***
D(LnINF)	−9.190***	−4.278***	128.162***	182.240***
D(LnGRE)	−11.343***	−4.732***	132.584***	131.817***

ADF: Fisher-Augmented Dickey Fuller; IPS: Im, Pesaran, and Shin; LLC: Levin, Lin, and Chu; PP: Fisher-Phillips Perron.

a: t statistics in the table; *p < 0.1, **p < 0.05, ***p < 0.01.

Model description

Generally, it is an ideal way to study the impact of research object by comparing the control group and the treatment group (e.g., the study of Xin et al.)⁴⁴ Furthermore, factorial design used in many impact studies⁴⁵ is a good approach to study the interactions of multifactors in different levels through experiments. Similarly, regression models can also study the interactions of multifactors based on the statistical analysis of existing data.

Compared with regression models, factorial design can get a more credible result through experiments, whereas regression models have a little bias because it is hard to completely meet assumptions of these models. However, factorial design usually requires that the research object can be artificially changed, whereas the macro factor/economic-social factors such as the industrial development and the urban development in this study cannot be changed as we expected. Therefore, this study applies a regression model rather than the factorial design to study the relationships of the five developments.

This study regards the five developments as a system in which there are plenty of two-way impacts between the green development, industrial development, agricultural development, urban development, and information development. Due to the two-way impacts, pooled ordinary least squares (POLS), fixed effect, and other simple regression models may have an endogenous problem that produces a bias estimation of coefficients, and it is critical to address the endogenous problem.

Compared with other simple estimation model, the vector autoregression (VAR) model can solve the endogenous problem between independent variables and dependent variables by establishing a simultaneous equation model consisted by $k$ equations. The left side of the VAR equations is the endogenous variables, while the right side is the lag items of the endogenous variables.

More importantly, compared with the results of some typical regression models such as POLS and fixed-effect model, the results of the VAR model are dynamic by using IRF, which can reveal the changes of impacts over time.

Furthermore, compared with the traditional VAR model, the improved PVAR model^46,47 includes the advantages of panel data. Because of this, it can expand the sample number and capture the influence by individual differences of the sample, thereby improving the robustness of regression results. Therefore, this study uses PVAR to conduct the empirical analysis. The model is established as Equation (1), where Equation (1) is a set of regression equations consisted by the five endogenous variables.

\begin{array}{l} (\begin{matrix} Δ L n I N D_{i, t} \\ Δ L n U R B_{i, t} \\ Δ L n A G R_{i, t} \\ Δ L n I N F_{i, t} \\ Δ L n G R E_{i, t} \end{matrix}) = M_{1} (\begin{matrix} Δ L n I N D_{i, t - 1} \\ Δ L n U R B_{i, t - 1} \\ Δ L n A G R_{i, t - 1} \\ Δ L n I N F_{i, t - 1} \\ Δ L n G R E_{i, t - 1} \end{matrix}) + M_{2} (\begin{matrix} Δ L n I N D_{i, t - 2} \\ Δ L n U R B_{i, t - 2} \\ Δ L n A G R_{i, t - 2} \\ Δ L n I N F_{i, t - 2} \\ Δ L n G R E_{i, t - 2} \end{matrix}) + L + M_{j} (\begin{matrix} Δ L n I N D_{i, t - j} \\ Δ L n U R B_{i, t - j} \\ Δ L n A G R_{i, t - j} \\ Δ L n I N F_{i, t - j} \\ Δ L n G R E_{i, t - j} \end{matrix}) + (\begin{matrix} α_{1, i} \\ α_{2, i} \\ α_{3, i} \\ α_{4, i} \\ α_{5, i} \end{matrix}) + (\begin{matrix} δ_{1, t} \\ δ_{2, t} \\ δ_{3, t} \\ δ_{4, t} \\ δ_{5, t} \end{matrix}) + (\begin{matrix} ε_{1, i t} \\ ε_{2, i t} \\ ε_{3, i t} \\ ε_{4, i t} \\ ε_{5, i t} \end{matrix}) \end{array}

(1)

where

i

denotes the region,

t

represents the period,

j

stands for the lag order,

M

is the 5 × 5 coefficient matrix of variables,

α

stands for the individual effect,

δ

stands for the time effect, and

ε

stands for the errors term.

For the determination of lag order $j$ , the main determined criteria include Akaike information criterion (AIC), Schwarz criterion (SC), and Hannan–Quinn information criterion (HQIC). If the results of different criteria are inconsistent, the lag order is usually determined by majority principles. Meanwhile, the lag order generally should not exceed fourth-order because the long lag order reduces the degree of freedom and make the model lack economic meaning. As shown in Table 2, the optimal values of SC and HQIC appear at the first-order lag, whereas the optimal value of AIC obtains the optimal value at the seventh-order lag. According to the above principles, the optimal lag orders of PVAR model in this study are first-order.

Table 2.

Test results of the lag order of the PVAR model.

Lag order	AIC	SC	HQIC
0	−25.446	−25.272	−25.378
1	−26.649	−25.602*	−26.239*
2	−26.587	−24.668	−25.837
3	−26.874	−24.081	−25.781
4	−27.442	−23.777	−26.008
5	−27.479	−22.941	−25.704
6	−27.627	−22.217	−25.511
7	−28.128*	−21.845	−25.670

AIC: Akaike information criterion; HQIC: Hannan–Quinn information criterion; SC: Schwarz criterion. * denotes the optimal value of each test.

Finally, the established PVAR model is required to be stable to ensure the feasibility of the IRF. Hence, the stability of the PVAR model needs to be tested. Specifically, the structure of the PVAR model will be stable if the reciprocal values of all characteristic roots of the characteristic equations, in established PVAR model, are less than 1 (i.e., within the unit circle). Figure 1 shows the reciprocal values of all characteristic roots which are generated by the PVAR model, indicating the all five reciprocal values are within the unit circle (i.e., less than 1). Therefore, the established PVAR model structure is stable, i.e., each equation in the PVAR model is convergent and thus it is appropriate to evaluate the dynamic relationships of the five developments by using the IRF.⁴⁸

Analysis of empirical results

The result of the PVAR model is shown in Equation (2). Because the PVAR model is a nontheoretical structural model with numerous coefficients that lack practical economic implications, it is necessary to use IRF to simulate the interactive relationships between the green development, industrial development, urban development, agricultural development, and information development.

\begin{array}{l} (\begin{matrix} Δ L n I N D_{i, t} \\ Δ L n U R B_{i, t} \\ Δ L n A G R_{i, t} \\ Δ L n I N F_{i, t} \\ Δ L n G R E_{i, t} \end{matrix}) = (\begin{matrix} - 0.2238 & 0.0389 & - 0.0484 & 0.0524 & 0.0621 \\ 0.0708 & 0.4000 & 0.0122 & 0.3077 & - 0.0148 \\ 0.2210 & 0.0354 & - 0.1154 & 0.1574 & - 0.0834 \\ 0.0121 & 0.2006 & 0.0248 & - 0.1355 & - 0.0244 \\ 0.0947 & 0.0149 & - 0.0097 & - 0.3572 & 0.1717 \end{matrix}) \\ \times (\begin{matrix} Δ L n I N D_{i, t - 1} \\ Δ L n U R B_{i, t - 1} \\ Δ L n A G R_{i, t - 1} \\ Δ L n I N F_{i, t - 1} \\ Δ L n G R E_{i, t - 1} \end{matrix}) + (\begin{matrix} α_{1, i} \\ α_{2, i} \\ α_{3, i} \\ α_{4, i} \\ α_{5, i} \end{matrix}) + (\begin{matrix} δ_{1, t} \\ δ_{2, t} \\ δ_{3, t} \\ δ_{4, t} \\ δ_{5, t} \end{matrix}) + (\begin{matrix} ε_{1, i t} \\ ε_{2, i t} \\ ε_{3, i t} \\ ε_{4, i t} \\ ε_{5, i t} \end{matrix}) \end{array}

(2)

The IRF presents the future values of endogenous variables after giving a standard deviation shock to the error term. Specifically, this study conducts the IRF for the five models, respectively. In addition, the data in terms of the green development (negative indicator) is reciprocated to make the image results consistent. The detailed results are shown in Figures 2 to 6.

IRF results of the industrial development

As shown in Figure 2, the current level of the industrial development is the main driving force for the future industrial development. Meanwhile, urban development, information development, and green development play a positive role in promoting the industrial development, whereas agricultural development has certain negative impacts on the industrial development. In terms of the strength of influence, industrial development > green development > urban development > information development > agricultural development (negative). Specifically, industrial development has the positive impacts on itself at the first time. These impacts reach a maximum after the first year, decrease with time, and approximately disappear after the fourth year. Meanwhile, urban development, information development, and green development have positive impacts on the industrial development after the first year. These impacts reach a maximum in the second year and decrease over time. Then, the impacts of the green development approximately disappear after the third year, the impacts of the urban development approximately disappear after the fourth year, and the impacts of the information development approximately disappear in the fifth year. Additionally, agricultural development has the negative impacts on the industrial development after the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fifth year.

IRF results of the urban development

As shown in Figure 3, the current level of the urban development is the main driving force for the future urban development. Moreover, information development is also a crucial factor for promoting the urban development. Meanwhile, industrial development and agricultural development have the positive impacts on urban development, whereas green development has certain negative impacts on the urban development. In terms of strength of influence, urban development > information development > industrial development > green development (negative) > agricultural development. Specifically, urban development has the positive impacts on itself at the first time; these impacts reach a maximum value after the first year, decrease over time, and approximately disappear after the eighth year. Therefore, the effect of the urban development is persistent. Meanwhile, information development has the positive impacts on the urban development since the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the seventh year; industrial development and agricultural development have the positive impacts on the urban development after the first year; the impacts reach a maximum in the second year, decrease over time. Then, the impacts of the agricultural development approximately disappear after the third year, while the impacts of the industrial development approximately disappear after the fifth year. Additionally, green development has a negative effect on the urban development after the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fourth year.

IRF results of agricultural development

As shown in Figure 4, the current level of the agricultural development is the main driving force for the future urban development. Meanwhile, industrial development, urban development, and information development play a certain role in promoting agricultural development, whereas green development has the negative impacts on the agricultural development. In terms of strength of influence, agricultural development > information development > industrial development > urban development > green development (negative). Specifically, industrial development has the positive impacts on the agricultural development since the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fifth year; urban development has the positive impacts on the agricultural development from the first year. These impacts reach a maximum in the third year, decrease over time, and approximately disappear after the sixth year; information development has the positive impacts on the urban development from the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fourth year. Additionally, green development has the negative impacts on the urban development from the first year. These impacts reach a maximum in the second year, and the negative impacts decrease over time and approximately disappear after the third year.

IRF results of the information development

As shown in Figure 5, the current level of the information development is the main driving force for the future information development. Moreover, urban development also plays a key factor in promoting the development of information. Meanwhile, industrial development and agricultural development have the certain impacts on the information development, whereas green development has the certain negative impacts on the agricultural development. In terms of strength of influence, information development > urban development > green development (negative) > industrial development > agricultural development. Specifically, industrial development has the positive impacts on the agricultural development from the first year. These impacts reach a maximum in the third year, decrease over time, and approximately disappear after the fourth year; urban development has the positive impacts on the agricultural development from the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the sixth year; agricultural development has the positive impacts on the urban development from the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the third year. Additionally, green development has the negative impacts on the urban development from the first year. These impacts reach a maximum in the second year, decrease over time, and the impacts approximately disappear after the third year.

IRF results of the green development

As shown in Figure 6, the current level of the green development is the main driving force for the future green development. Meanwhile, industrial development has a certain role in promoting the green development, whereas information development, urban development, and agricultural development have the certain negative impacts on the green development. In terms of strength of influence, green development > information development (negative) > urban development (negative) > industrial development > agricultural development (negative). Specifically, green development has the positive impacts on itself from the first time. These impacts reach a maximum in the first year, decrease over time, and disappear after the fourth year. Meanwhile, industrial development has the positive impacts on the urban development from the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fourth year. Additionally, urban development has the negative impacts on the urban development from the first year. These impacts reach a maximum in the third year, decrease over time, and disappear after the sixth year; agricultural development has the negative impacts on the urban development from the first year. These impacts reach a maximum in the third year, decrease over time, and approximately disappear after the fourth year; information development has the negative impacts on the urban development from the first year. These impacts reach a maximum in the second year, decrease over time, and approximately disappear after the fifth year.

Conclusions and recommendations

Based on theoretical analysis, this study establishes the PVAR model by integrating the five developments as a whole to evaluate the dynamic impacts between the five developments. The following main conclusions are drawn: (i) The delivery times of impacts between each development were generally one year and the impacts decreased over time. Then, most impacts gradually disappeared after fourth year, whereas the impacts of the urban development on itself had a longer duration time. (ii) The main driving force that promotes each development was their own current development level. Meanwhile, the mutual promotions from other developments were also the principal factors. However, these impacts showed the different strengths and trends over time. (iii) There were inconsistencies between the five developments: information development and urban development had the negative impacts on the green development; agricultural development could not promote the industrial development; and green development had certain constraints on the urban development, agricultural development, and information development.

In light of the findings, some recommendations are given: It is recommended to enhance the construction of energy-saving buildings, increase the proportion of low-emission or electricity-driven vehicles, encourage energy-saving lifestyles, and cultivate low energy intensive industries, to promote urban green development. It is recommended to improve the production, sale, and application of energy-saving information products, adopt the energy-saving design, strengthen the operation of information facilities, use the information technologies to upgrade traditional industries, enhance the dynamic energy management, to promote the low-carbon information development. It is recommended to increase agricultural investment in the Research & Development, strengthen the use of green technology in agriculture, improve the proportion of energy-saving agricultural machines and equipments, and develop ecological agriculture, to promote the agricultural green development. It is recommended to develop the precision agriculture and high-tech agriculture, enhance scale management, save land area and reduce redundant labor, cultivate the agricultural talents, expand the supply of industrial raw materials, to enhance the support of agriculture on the industry. It is recommended to formulate the plan of transportation, housing, and other infrastructure from a long-term perspective, ensure the coverage and quality of medical care, education, and social security, optimize the industrial structure and develop new industries, to improve the ability of population absorption and to enhance the basis for sustainable urban development.

Highlights:

Conflicts between the green development and other factors are evaluated.

Inner mechanisms of the modernization strategy are analyzed.

PVAR model that uses panel data is established.

IRF is performed to study the dynamic impacts.

Footnotes

Acknowledgements

The authors would like to thank the efforts by the editor and all anonymous reviewers.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Natural Science Foundation of China (No. 71573114 and 71503049).

Wei Li is a PhD candidate at Fuzhou University since 2016 and a research assistant at Nanyang Technological University. His interests are energy & environmental economics and green growth.

Yongqin Xi is a Professor at Fuzhou University and has published over one hundred papers related to the circular economy and environmental economics.

Jiayang Lu is a student at Fuzhou University and has published five papers in recent years. Her research interest is environmental economics.

Feimei Wu is a Professor at Minjiang University and has published over thirty papers related to the circular economy and environmental management.

Pengfei Wu is a student at Chinese Academy of Social Sciences and has published a few papers in recent years. His research interest is green finance.

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