Abstract

A coordinated nexus of agricultural resources is vital to achieve food security and sustainable development in China. Comprehensively considering the water–energy–food nexus as well as the external environment, this study adopts a three-stage data envelopment analysis (DEA) modelling evaluation method to assess the agricultural production efficiency (APE) of seven provinces in the middle and lower reaches of the Yangtze River (MLYR) during 1996–2015. The results show that the three-stage DEA modelling evaluation method reveals real APE and is considered to be a better quantitative method than conventional approaches. A gradually widening range of APE is an important challenge for this region. Significantly, this region generates huge demands for agricultural resources. Moreover, regional emissions of greenhouse gases (GHG) decreased from 34.20 million tons standard coal in 1996 to 32.11 million tons standard coal in 2015, though APE has continued to decrease by 2.56% in the past two decades. In general, the management and technology levels should be improved simultaneously, even though specific opportunities for APE improvement vary across provinces in MLYR. However, understanding the temporal and spatial variation of APE along with the WEF nexus from a production-based insight is a vital step toward appropriately targeted policy making for nationwide resources savings and emissions reduction.

Introduction

It is estimated that the available water resources for humans is 7,000 km3 (Shi, 2002), the global demand for food (excluding bioenergy) is 2,887 million tons (FAO, 2002; Alexandratos & Bruinsma, 2012), and the demand for energy is 7,740 million barrel of oil equivalents (BOE) (International Energy Agency, 2006). However, global trends of growing populations, increasing living standards and speeding urbanisation have been intensifying the demands for water, energy and food in recent decades, and thus are causing broad public concerns (Waughray, 2011; Wang et al., 2017). At present, about 1,100 million people are without adequate access to safe water, close to 1,000 million people are undernourished and 1,500 million people are without access to modern forms of energy (Finley & Seiber, 2014). The United Nations Food and Agriculture Organisation (FAO) projects a 50% increase in demand for food by 2030, and the International Food Policy Research Institute (IFRI) expects a 30% increase in demand for water, with the International Energy Agency (IEA) forecasting that the world economy will demand at least 40% more energy by 2030 (World Economic Forum, 2011). For such increased demand for water, energy and food (WEF) to be realised, significant and perhaps radical changes in WEF resources consumption will be required (Zheng et al., 2018). Consequently, it is necessary to make qualitative and quantitative evaluations of WEF from the production–consumption perspective (Allouche et al., 2015; Li et al., 2016). Case study methods combined with models have been used to quantitatively explore the interconnections of the WEF nexus in varied regions. Smidt et al. (2016) analysed the relationship of the WEF nexus over the High Plains Aquifer in the central United States as a framework to isolate the major drivers that have shaped its history. Yang et al. (2016a) evaluated the interconnection of the WEF nexus in the Indus River of Pakistan using a revised Indus Basin Model. Moreover, Mi & Zhou (2010), Gulati et al. (2013) and Sahin et al. (2014), attempted to explore the WEF nexus using intelligent simulation, system dynamics and multivariate statistical analysis, respectively. Meanwhile, other studies qualitatively assessed the impacts of WEF inputs and outputs on regional development and the environment (e.g., Allouche et al., 2015; Foran, 2015; Al-Saidi & Elagib, 2017).

In general, the above studies mainly assess the status and trends of WEF from a holistic viewpoint, to contribute to better inform policy making and maintain regional sustainable development. However, agriculture, it turns out, is at the centre of the WEF nexus around the world. Agricultural production consumes 70% of the earth's fresh water (Xinchun et al., 2017; Zhang et al., 2018), and consumes 30% of the world's available energy (for the production of synthetic fertilisers and the powering of irrigation systems, farm machinery and distribution). There are few studies on the WEF nexus in agricultural production. Moreover, agriculture still has a central role in ensuring the food security and welfare of people across the world, which is especially true in China (Piao et al., 2010; Ding et al., 2017); the manner and extent to which agriculture is exploited also impact on the environment (e.g., Brentrup et al., 2004; Scott et al., 2011; Adachi et al., 2014) and on regional climate change (e.g., Wang et al., 2007; Wang et al., 2012a; Howells et al., 2013; Konzmann et al., 2013; Wang et al., 2013a; Conway et al., 2015; Räsänen et al., 2015; Khan et al., 2016; Yang et al., 2016b; Choi & Qu, 2017; Haro-Monteagudo et al., 2017). For example, as one of the most important economic sectors, agriculture in China contributes 11% to GDP, whilst meanwhile feeding some 22% of the global population with only 7% of the world's arable lands (NBSC, 2009). In addition, in China, arable lands (130 Mha) span temperate, subtropical and tropical climates, and agriculture is consequently one of the four key areas identified for adaptation to climate change, as set out in China's national climate change project (NDRC, 2007).

As mentioned above, apart from by unsustainable resource consumption, the WEF nexus is also affected by the ongoing low utilisation efficiency in relation to agricultural production (Endo et al., 2015a; Zhang et al., 2015). Hence, in our case, agricultural production efficiency (APE) was adopted as a measure of the ‘resource nexus to sustainable development’ (e.g., World Economic Forum, 2011; Hanumankar, 2014; Endo et al., 2015b) to manifest the synergy and contradiction of the WEF nexus, which goes beyond simply the paradox of ‘one aspect wanes, the other waxes’. Emphasising that the perception of agriculture production correlated with WEF nexus issues should be transformed into a critical solution for sustainability and global development. Moreover, the FAO describes the nexus as ‘a new approach in support of sustainable agriculture’ (Mohtar & Lawford, 2016), due to the fact that agricultural production is becoming increasingly resource-intensive through increased use of water and energy (including electricity, fertiliser, diesel fuel, pesticides and machinery) to produce more food (Ringler et al., 2013). Accordingly, the application of APE has evolved to a broader scope at regional levels. For example, based on the shortage and waste situation of water resources, Wang et al. (2012a) selected four indicators and attempted to evaluate the impacts of agricultural water resource utilisation on APE in China at country level, and analysed the regional differences with GIS technology. Gołaszewski et al. (2012) presented an input–output approach to evaluate the responses of energy consumption to APE in Poland, indicating that the prospects for energy savings in agricultural production stem from the use of mineral fertilisers and reduction in consumption of fuel per unit of area. All of the above studies on regional APE shed light on the long-term competitiveness advantages of a region or a country, helping governments to precisely characterise local conditions and target region-specific problems, which are prerequisites for robust policymaking.

How can we understand the concept that the demand for one resource can drive the demand for another and quantify production efficiency? In this study, the data envelopment analysis (DEA) model offers us the capability to explore the WEF nexus from the perspective of APE. In fact, the DEA model has also been used to evaluate the impacts of the WEF nexus on the external environment (e.g., Qiu et al., 2008; Liao, 2011; Haie, 2015). In 1978, three famous statisticians (Charnes, Cooper and Rhodes) first proposed the DEA model, which is a nonparametric model for evaluating the relative efficiency of a set of decision-making units (DMUs) that contain multiple input and output variables (Charnes et al., 1978). Since then, this representative efficiency measurement tool has become known as the CCR-DEA model. In addition, the DEA model has been fully developed and improved during subsequent decades (Färe & Grosskopf, 1996, 2000). At present, the driving resource-oriented and managing process-based BCC-DEA model, under the principle of input minimisation and output maximisation, is currently considered the most prevalent method to characterise production efficiency (Chen et al., 2014). Based on the previous characteristics of the BCC-DEA model, it offers an opportunity for us to use the model to quantify APE and the impacts of the WEF nexus on the external environment. Unfortunately, as for all DEA models, the evaluation of production efficiency is always subject to the internal mismanagement of the model and disturbances derived from external environmental variables (Dyckhoff & Allen, 2001; Chen et al., 2014; Zhang et al., 2017). Therefore, for the internal mismanagement of the model, adopting the multi-stage DEA modelling evaluation method can effectively separate the impacts of internal mismanagement of model on the efficiency calculation of DMUs.

Multi-stage methods are widely used for evaluation of production efficiency, including the two-stage (Wang et al., 1997), three-stage (Fried et al., 2002) and four-stage methods (Fried et al., 1999). However, whether two-stage or four-stage methods are used, identifying and eliminating the effect of external environmental variables on the quantification of efficiency has not yet been possible (Zhang et al., 2017). Hence, the DEA is considered as a nonparametric frontier efficiency analysis model, based on the stochastic frontier production function (SFA) (Aigner et al., 1977; Battese & Corra, 1977; Jondrow et al., 1981; Battese & Coelli, 1992). To address the previous drawback, Fried et al. (2002) developed the three-stage DEA modelling evaluation method by constructing a parametric stochastic frontier analysis (SFA) approach with a non-parametric DEA approach to eliminate the impacts of both the external environment and statistical noise on efficiency measurement, with the goal of revealing the real efficiency level of DMU.

The conception of the ‘WEF-nexus’ is the most recent reminder of the ongoing sharp increase in consumption of resources and integrated thinking is needed to address this global change and challenge (Al-Saidi & Elagib, 2017). Moreover, the relevant environmental problems and regional effects of climate change which are derived from the WEF nexus also remain a major challenge in China and rest of the world (Ma et al., 2012; Beck & Walker, 2013; Wang et al., 2013b; Sun et al., 2017). Hence, in-depth studies on quantifying APE and evaluating the responses of the WEF nexus to it are imperative, motivating our present research. Specifically, based on the three-stage DEA modelling evaluation method, we offer a comprehensive assessment of APE and have quantified the optimal inputs of agricultural resources as well as the impacts of APE on the external environment in the MLYR, China during the period 1996–2015. The results are expected to contribute to a better understanding of the impacts of the WEF nexus on the external environment in the field of agricultural production. This will be beneficial for formulating regional strategies against the potential menaces of wasted resources and environmental contamination.

Furthermore, multi-scale analytical methods coupled with a hydrological, energy and crop model, the interrelationship of the WEF nexus in the context of climate change. And an early warning system for climate risk will all also be discussed as an extension to this paper, with the aim of further exploring the forces of this paradigm and how they interact at different scales.

Methods and materials

Study sites

The Yangtze River, regarded as the ‘Natural Moat’ is the first longest river in China and crosses the country from west to east. The MLYR is the main component of the ‘Yangtze River economic zone’, located in central and eastern China (between 111°E–123°E and 27°N–34°N), with a total area of more than 200,000 km2 and including seven provinces: Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei and Hunan (Figure 1). The climate of this region is of the subtropical monsoon type with summer rainfall in June to August. The mean annual precipitation is 1,000–1,400 mm and the annual mean temperature varies from 14–18 °C. The region is an important manufacture base for China, including steel, machinery, electric power, textiles and the chemical industry, etc. Agriculture is also well developed, the land reclamation index is at a high level, and it is an important base for grain, cotton and oil. In 2015, the GDP, population and crop yield of this area were US$3,456,700 million, 393 million people and 16 million tons, respectively, accounting for approximately 34%, 29% and 26%, respectively, of the corresponding totals in China.

Fig. 1.

Study area, including Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei and Hunan, represented by the shaded areas. In addition, in the right-hand figure, the dark border is the Yangtze River basin; the unshaded areas are the other provinces of the Yangtze River economic zone, including Chongqing, Sichuan, Guizhou and Yunnan.

Fig. 1.

Study area, including Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei and Hunan, represented by the shaded areas. In addition, in the right-hand figure, the dark border is the Yangtze River basin; the unshaded areas are the other provinces of the Yangtze River economic zone, including Chongqing, Sichuan, Guizhou and Yunnan.

Basic definition of the DEA model

The DEA model uses four essential definitions: the decision making unit (DMU) (Luo, 2012a), production possibility set (PPS) (Banker et al., 1984; Yu et al., 1996), production frontier surfaces (PFS) (Wei, 2001) and production efficiency (which includes scale efficiency, technical efficiency and overall efficiency) (Debreu, 1951; Farrell, 1957). Each of these is discussed below.

Three-stage DEA model evaluation method

First stage: measurement of comprehensive production efficiency

The DEA model includes both input-oriented and output-oriented types which depend on the problem-solving view adopted by researchers. The evaluative principle refers to minimising the use of input for a given production output or to maximise the production output of given inputs. Because the input variables are usually representative they are more controllable than output variables (Zhang et al., 2017); this study adopts the input-oriented BCC model: see Formula (1): 
formula
(1)
where is DMUs N × 1 vector of inputs, is DMUs M × 1 vector of outputs, is an n × 1 vector of intensity variables, is the non-Archimedean, are respectively the input-slack variable and output-slack variable, is the efficiency of an input-oriented BCC model. The optimal solution of formula (1) is (), and:
  • (1)

    If , then DMU is non-efficient;

  • (2)

    If , and or , then DMU is weakly-efficient;

  • (3)

    If , and , then DMU is efficient.

Second stage: eliminate the external environment and statistical noise

In addition to comprehensive production efficiency, in the first-stage, the DEA model also calculates the slacks of each DMU in terms of input, which represent the difference between actual and ideal inputs in terms of achieving optimal production efficiency (Zhang et al., 2017). Hence, towards these input slacks, in the second-stage, an SFA-regression model is built to deal with the slacks and the external environment variables, which mainly include managerial inefficiency variables and variables of disturbance of statistical noise, as in Formula (2): 
formula
(2)
where is the slack value of n'th input of i’th DMU; is the disturbance of statistical noise; is the coefficient of the disturbance of statistical noise; are the mixed error terms, indicates the random interference, and indicates that the management is inefficient. Moreover, and .
Furthermore, in the SFA-regression model, () is the error term, where is a normal error term representing pure randomness and is a non-negative error term representing technical inefficiency. The entire () is easily estimated for each observation but a previously unsolved problem was how to separate it into its two components, and . Therefore, considering the expected value of , conditional on (), Jondrow et al. (1981) developed an explicit formula (see Formula (3)), which is given for the half-normal and exponential cases, and which suggested a solution to this problem of the calculation of Formula (2). 
formula
(3)
where is the adjusted input; is the original input; is the adjustment of the disturbance of statistical noise; and is the term which places all DMUs under the same management level.
In addition, the conditional expectation of managerial inefficiency is obtained by Formula (4) and Formula (5) (Chen et al., 2014). 
formula
(4)
 
formula
(5)
 
formula
where is the probability density function of the standard normal distribution; and is the accumulative probability function of the standard normal distribution (Luo, 2012b).

Third stage: real production efficiency measurement

Adopting the adjusted inputs derived from the second-stage processing and the original outputs in the first-stage modelling, the real level of APE of the study region can be obtained.

Malmquist index

The Malmquist index was developed on the basis of the boundary method, which can be used to depict productivity with a distance function (Malmquist, 1953). It assumes that there is a production possibility set S' which is shown in Formula (6). S' represents the ability to achieve the transformation of x’ to y’, and the point (x’, y’) in the set S' at which it can achieve the largest output y’ in every given input x’ is the production frontier. With production possibility set S', the distance function in time t (1, 2 … T) is shown in Formula (7). 
formula
(6)
 
formula
(7)
where D’ (x’, y’) 1, if point (x’, y’) S'; and D’ (x’, y’) = 1, then point (x’, y’) is at the production frontiers (Cobb & Douglas, 1928). The Malmquist index is defined as Formula (8). 
formula
(8)
where Dt and Dt+1 are respectively two distance functions, in time t and t + 1, which change in production technology.

Moreover, setting 1 as the criterion, if the value of the Malmquist index (M value) is larger than 1, this means that the production efficiency is rising. Alternatively, if the M value is less than 1, this means that the production efficiency is declining. The Malmquist index can also be decomposed into the technology progress index (Tch) and the technical efficiency change index (Ech). Under the assumptions of constant returns to scale, the Ech can be subdivided into the pure technical efficiency change index (Pech) and the scale efficiency changes index (Sech).

Variables classification

The DEA model is developed based on the input and output variables of DMUs. In this paper, the amount of irrigation water, effective rainfall, agricultural electricity consumption, agricultural fertiliser consumption, agricultural diesel fuel usage and agricultural pesticide usage were selected as the input variables. One part of the output variable is the total grain output. In addition, the environment index and climate change index can be regarded either as inputs or as undesirable outputs in a DEA model (Dyckhoff & Allen, 2001). Hence, agricultural waste water, agricultural waste gas and greenhouse gases are chosen as other parts of the output variable (Korhonen & Luptacik, 2004). The gross domestic product and annual average temperature are treated as the external environment variables, considering that changing these variables would interfere with the model. Therefore, the DEA model in this study includes six input variables, four output variables and two external environment variables. The variables classification are illustrated in Table 1. In addition, the framework of evaluation of the WEF nexus is shown as Figure 2.

Table 1.

Classification indicators.

Variable Index Unit 
Input Water resources Amount of irrigation water 100 million m3 
Effective rainfall mm 
Energy resources Rural electricity consumption 100 million kwh 
Agricultural fertiliser consumption 10,000 tons 
Agricultural diesel fuel usage 10,000 tons 
Pesticide use 10,000 tons 
Output Food production Total grain output 10,000 tons 
Environment Agricultural wastewater 10,000 tons 
Agricultural waste gas gram 
Climate change Greenhouse gases (carbon dioxide) gram 
External environment GDP 100 million Yuan 
Annual average temperature  
Variable Index Unit 
Input Water resources Amount of irrigation water 100 million m3 
Effective rainfall mm 
Energy resources Rural electricity consumption 100 million kwh 
Agricultural fertiliser consumption 10,000 tons 
Agricultural diesel fuel usage 10,000 tons 
Pesticide use 10,000 tons 
Output Food production Total grain output 10,000 tons 
Environment Agricultural wastewater 10,000 tons 
Agricultural waste gas gram 
Climate change Greenhouse gases (carbon dioxide) gram 
External environment GDP 100 million Yuan 
Annual average temperature  
Fig. 2.

Evaluation framework of the WEF nexus.

Fig. 2.

Evaluation framework of the WEF nexus.

Data sources

Considering the prerequisite of data, of which the DEA model needs a sufficient number of DMUs, a ‘rule of thumb’ suggests that the number of DMUs (K), the number of input indicators (M) and the number of output indicators (N) should satisfy: (M+ N) ≤ K (Wang et al., 2015).

The data used in this study are primarily obtained from statistical systems in China. To be more specific, regional agricultural inputs and outputs were obtained from China's statistical yearbooks, agricultural census bulletin data of China, water resources bulletin and province-specific statistical yearbooks. The agricultural SO2, NOx, CO, HC and CO2 emissions were obtained from China's environmental state bulletin. The meteorological data was obtained from the National Meteorological Information Centre of China (NMIC), including the annual average temperature and the rainfall of a period of ten days. The time coverage of all research data is 1996–2015.

Results

Parameters and estimation of the SFA model

The SFA model was used to analyse input slack variables, including the slacks of irrigation water, effective rainfall, agricultural electricity, agricultural fertiliser, agricultural diesel fuel and agricultural pesticide. Two external environmental variables – gross domestic product and annual average temperature – were used as the independent variables of slacks.

Table 2 shows that for two environmental variables and input slacks, the LR test value of one-sided error is greater than the critical value of the mixed chi-square distribution test and is under the 0.5% significance level, indicating the fair robustness of the overall model. The value of all input variables are greater than 0.6, implying that the managerial factors have dominant effects on inefficiency, and it is necessary to separate the effects of the managerial factor and statistical noise using the SFA model (Zhang et al., 2017).

Table 2.

Parameters and estimation of the SFA model.

Slack Constant term GDP Annual average temperature Sigma-squared Gamma LR test of one-sided error No. iterations 
Irrigation water −121.74 0.00 5.91 10,440.42 0.99 94.38 99 
Effective rainfall −172.55 0.00 5.53 26,154.77 0.96 52.82 100 
Agricultural electricity −177.48 0.00 9.76 13,641.96 0.74 59.02 100 
Agricultural fertiliser −119.27 0.00 5.74 5,287.52 0.94 51.90 100 
Agricultural diesel fuel −54.63 0.00 2.81 2,659.04 0.73 81.32 19 
Agricultural pesticide −7.81 0.00 0.37 27.96 0.97 42.16 57 
Slack Constant term GDP Annual average temperature Sigma-squared Gamma LR test of one-sided error No. iterations 
Irrigation water −121.74 0.00 5.91 10,440.42 0.99 94.38 99 
Effective rainfall −172.55 0.00 5.53 26,154.77 0.96 52.82 100 
Agricultural electricity −177.48 0.00 9.76 13,641.96 0.74 59.02 100 
Agricultural fertiliser −119.27 0.00 5.74 5,287.52 0.94 51.90 100 
Agricultural diesel fuel −54.63 0.00 2.81 2,659.04 0.73 81.32 19 
Agricultural pesticide −7.81 0.00 0.37 27.96 0.97 42.16 57 

Note: the critical value of the mixed chi-square distribution test = 9.634 and is under the 0.5% significance level.

Analysis of comprehensive production efficiency

Table 3 presents the real APE (eliminating the impacts of external environment and statistical noise) of the MLYR from 1996 to 2015. The APE has continued to decrease in the past two decades, decreasing from 1.000 in 1996 to 0.975 in 2015. Regionally, however, the difference is obvious. The Jiangsu province has the highest efficiency (0.9997), whereas Shanghai presents the lowest value (0.9555). A closer look at provincial APE shows that the top to bottom provinces are respectively: Jiangsu, Hunan, Zhejiang, Anhui, Jiangxi, Hubei and Shanghai.

Table 3.

Real APE in the middle and lower reaches of the Yangtze River in China (1996–2015).

Year Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan Region 
1996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 
1997 1.000 1.000 0.998 1.000 1.000 1.000 1.000 1.000 
1998 1.000 1.000 1.000 1.000 0.980 1.000 1.000 0.997 
1999 1.000 1.000 1.000 1.000 0.995 0.994 1.000 0.998 
2000 0.994 1.000 0.997 0.990 0.974 0.973 0.998 0.989 
2001 1.000 1.000 1.000 1.000 0.962 0.960 0.998 0.989 
2002 1.000 1.000 1.000 1.000 0.959 0.949 0.989 0.985 
2003 0.921 1.000 0.993 1.000 1.000 0.969 0.982 0.981 
2004 1.000 0.999 1.000 0.991 0.990 0.965 0.998 0.992 
2005 0.991 1.000 1.000 0.986 0.985 0.961 1.000 0.989 
2006 0.991 1.000 1.000 0.985 1.000 0.963 1.000 0.991 
2007 0.979 1.000 1.000 1.000 1.000 0.965 1.000 0.992 
2008 0.957 0.996 0.995 0.990 0.998 0.981 0.998 0.988 
2009 0.900 1.000 0.994 0.991 1.000 0.989 1.000 0.982 
2010 0.860 1.000 0.994 0.991 0.993 0.997 1.000 0.976 
2011 0.931 1.000 0.993 1.000 1.000 1.000 1.000 0.989 
2012 0.937 1.000 1.000 1.000 1.000 1.000 1.000 0.991 
2013 0.918 1.000 1.000 1.000 1.000 1.000 0.999 0.988 
2014 0.897 1.000 1.000 1.000 1.000 1.000 1.000 0.985 
2015 0.833 1.000 0.994 1.000 1.000 1.000 1.000 0.975 
Mean 0.9555 0.9997 0.9979 0.9962 0.9918 0.9833 0.9981 
Year Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan Region 
1996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 
1997 1.000 1.000 0.998 1.000 1.000 1.000 1.000 1.000 
1998 1.000 1.000 1.000 1.000 0.980 1.000 1.000 0.997 
1999 1.000 1.000 1.000 1.000 0.995 0.994 1.000 0.998 
2000 0.994 1.000 0.997 0.990 0.974 0.973 0.998 0.989 
2001 1.000 1.000 1.000 1.000 0.962 0.960 0.998 0.989 
2002 1.000 1.000 1.000 1.000 0.959 0.949 0.989 0.985 
2003 0.921 1.000 0.993 1.000 1.000 0.969 0.982 0.981 
2004 1.000 0.999 1.000 0.991 0.990 0.965 0.998 0.992 
2005 0.991 1.000 1.000 0.986 0.985 0.961 1.000 0.989 
2006 0.991 1.000 1.000 0.985 1.000 0.963 1.000 0.991 
2007 0.979 1.000 1.000 1.000 1.000 0.965 1.000 0.992 
2008 0.957 0.996 0.995 0.990 0.998 0.981 0.998 0.988 
2009 0.900 1.000 0.994 0.991 1.000 0.989 1.000 0.982 
2010 0.860 1.000 0.994 0.991 0.993 0.997 1.000 0.976 
2011 0.931 1.000 0.993 1.000 1.000 1.000 1.000 0.989 
2012 0.937 1.000 1.000 1.000 1.000 1.000 1.000 0.991 
2013 0.918 1.000 1.000 1.000 1.000 1.000 0.999 0.988 
2014 0.897 1.000 1.000 1.000 1.000 1.000 1.000 0.985 
2015 0.833 1.000 0.994 1.000 1.000 1.000 1.000 0.975 
Mean 0.9555 0.9997 0.9979 0.9962 0.9918 0.9833 0.9981 

In terms of overall efficiency (crste), scale efficiency (scale) and technical efficiency (vrste) (see Figure 3), Jiangsu province has the highest crste efficiency (0.9996), whereas Shanghai presents the lowest value (0.9332). Meanwhile, Jiangsu, Anhui, Jiangxi and Hunan provinces have the highest vrste efficiency (1.0000), whereas Zhejiang presents the lowest value (0.9996). Furthermore, Jiangsu province has the highest scale efficiency (0.9996), whereas Shanghai presents the lowest value (0.9334). Consequently, vrste efficiency represents the overall performance of production efficiency, scale efficiency reflects changing efficiency accompanied by investment scale, and crste efficiency reflects the production efficiency impacted by technical innovation.

Fig. 3.

Mean values (from 1996–2015) of overall, technical and scale efficiency in the study area (Crste represents overall efficiency; vrste represents technical efficiency; and scale represents scale efficiency).

Fig. 3.

Mean values (from 1996–2015) of overall, technical and scale efficiency in the study area (Crste represents overall efficiency; vrste represents technical efficiency; and scale represents scale efficiency).

Furthermore, Figure 4 presents the agricultural productive frontier surfaces. Observing the three efficiencies, if they overlap it also means that the APE is optimal.

Fig. 4.

The productive frontier surfaces (1996–2015) of: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Fig. 4.

The productive frontier surfaces (1996–2015) of: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Optimal input of agricultural resources

Figure 5 presents the optimal inputs of agricultural resources, including irrigation water, effective rainfall, agricultural electricity, agricultural fertiliser, agricultural diesel fuel and agricultural pesticide for seven provinces (1996–2015 on average) after eliminating the impacts of external environment and statistical noise.

Fig. 5.

The optimal resource inputs for the three-stage DEA modelling evaluation method: (a) amount of irrigation water; (b) effective rainfall; (c) agricultural electricity consumption; (d) agricultural diesel fuel usage; (e) agricultural fertiliser consumption; and (f) agricultural pesticide usage.

Fig. 5.

The optimal resource inputs for the three-stage DEA modelling evaluation method: (a) amount of irrigation water; (b) effective rainfall; (c) agricultural electricity consumption; (d) agricultural diesel fuel usage; (e) agricultural fertiliser consumption; and (f) agricultural pesticide usage.

It can be seen that Jiangsu province consumes the most irrigation water, effective rainfall, agricultural electricity and agricultural fertiliser. Because of the large agricultural population, extensive cultivated lands and modern agricultural industry, it consumes tremendous resources. In terms of agricultural diesel fuel, this is especially true of Zhejiang province, which consumes the most. This result could be attributable to the province's geography-based characteristic: with nearly 74.63% area being mountains and hills, a great deal of water-saving irrigation equipment has had to be fitted on the mountains and hills to satisfy the standards of modern agriculture. In addition, Hubei and Hunan provinces consume the most pesticides which is due to them being the primary rice production regions in China. Moreover, the high humidity and temperature in these provinces makes them a breeding ground for agricultural pests and diseases and, therefore, chemical pesticides are used frequently.

Variation of production efficiency based on Malmquist index

With the Malmquist index value (M value) presented in Figure 6, we further explore reasons for the fluctuation of efficiency in the varied regions. Results show that the M value of most years is less than one, which means that their input–output efficiencies are all decreasing, conforming to our earlier results using the DEA model.

Fig. 6.

Variations in the Malmquist index (1996–2015) in: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Fig. 6.

Variations in the Malmquist index (1996–2015) in: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Impacts of agricultural production efficiency on the external environment

The emissions of waste gas from the agricultural production processes are listed in Figure 7. Taking Shanghai for example, the amount of agricultural waste gas was at a high level in 1996, and a large reduction can be found from 1997–2015 (see Figure 7(a)). This is because the development of industry and urbanisation gradually replaced agriculture with the reform and opening up of Shanghai. However, the total emissions of Jiangsu, Zhejiang and Anhui remain basically unchanged during a period of two decades (see Figure 7(b)–7(d)). This is owing to the fact that the proportion of agricultural industry and the structure of energy resources are essentially unchanged, causing the tremendous consumption of fossil fuels, fertilisers and pesticides. Meanwhile, the emissions of Jiangxi province have had an evident decrease since 1998 (see Figure 7(e)); as in Jiangxi, the amount of agricultural waste gases in Hubei decreased from 1998 but began to increase again from 2006 (see Figure 7(f)). Furthermore, a closer look at Hunan's agricultural waste gases show that the increase in emissions lasted from 2004 to 2012 (see Figure 7(g)).

Fig. 7.

Quantification of waste gas emissions from agricultural production (1996–2015) in: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Fig. 7.

Quantification of waste gas emissions from agricultural production (1996–2015) in: (a) Shanghai; (b) Jiangsu; (c) Zhejiang; (d) Anhui; (e) Jiangxi; (f) Hubei; and (g) Hunan.

Figure 8 presents the results for wastewater from the process of agricultural production. It shows that there is a noticeable incremental process of agricultural waste water during the period of 1996–2015 (i.e., in Hunan, Anhui, Hubei, Jiangsu, Zhejiang, Jiangxi and Shanghai, ordered from most to least). In addition, the discharge of agricultural waste water will cause a series of serious environmental problems. Hence, for massive agricultural waste water discharge, efficient solutions should be sought which provide optimal production efficiency with fewer resource inputs (Brentrup et al., 2004).

Fig. 8.

Amount of agricultural waste water in the study area (1996–2015).

Fig. 8.

Amount of agricultural waste water in the study area (1996–2015).

In terms of the greenhouse gases which are emitted from agricultural activity, the GHG emission load is shown in Figure 9, with details of the seven studied provinces presented in Table 4. Using a statistical approach to translate the carbon oxides into standard coal, the holistic emissions of GHG have continued to decrease in the past two decades, decreasing from 34,201,287 ton standard coal in 1996 to 32,116,768 ton standard coal in 2015. Regionally, however, the differences are obvious. Jiangsu province has the highest emission (8,591,648 ton standard coal), whereas Shanghai presents the lowest value (404,533 ton standard coal).

Table 4.

Carbon dioxide amount, transformed into standard coal (in tons).

Region
 
Year Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan 
1996 1,103,386.6 8,540,021.0 4,640,474.1 6,480,509.1 2,820,934.9 6,217,680.9 4,398,281.2 
1997 394,994.8 8,778,633.0 4464,999.8 5,601,908.6 2,761,413.2 6,787,656.5 4,314,551.4 
1998 445,855.1 9,050,316.1 4,318,126.2 5,714,030.2 2,548,464.3 7,418,563.6 4,406,228.9 
1999 643,993.4 8,877,240.3 4,427,406.7 5,619,780.0 2,567,081.4 6,026,241.6 4,381,528.0 
2000 600,459.5 8,813,935.7 4,453,806.3 5,538,325.8 2,256,888.8 6,150,546.1 4,390,512.3 
2001 594,424.7 8,757,034.5 4,611,358.0 5,974,051.5 2,285,534.8 5,930,480.7 4,440,167.3 
2002 500,030.1 8,685,949.0 4,594,421.3 5,623,302.4 2,247,298.7 6,186,149.3 4,402,041.9 
2003 395,099.9 8,579,249.3 4,481,302.3 5,716,690.6 2,160,861.0 6,344,999.6 4,438,791.4 
2004 384,585.7 8,583,189.9 4,674,776.8 5,384,721.3 2,312,481.6 6,597,709.2 4,735,019.3 
2005 362,699.4 8,598,156.5 4,716,999.9 5,420,641.7 2,349,979.5 6,570,094.6 4,854,024.9 
2006 354,678.6 8,619,766.1 4,725,490.5 5,507,446.8 2,397,096.9 6,548,582.1 4,898,091.6 
2007 336,627.3 8,612,209.8 4,706,793.6 5,516,768.8 2,266,831.6 6,711,557.6 4,942,468.1 
2008 357,910.9 8,567,932.6 4,575,887.0 5,537,078.1 2,234,177.6 7,053,974.2 4,925,408.4 
2009 258,588.5 8,712,547.3 4,553,966.7 5,568,153.1 2,219,596.9 7,240,956.3 5,037,567.9 
2010 252,507.5 8,694,639.8 4,624,583.2 5,608,471.7 2,223,354.7 7,409,408.2 5,114,417.9 
2011 260,001.9 8,502,913.0 4,633,126.6 5,729,075.6 2,257,484.2 7,562,754.8 5,215,651.6 
2012 218,617.0 8,344,685.5 4,632,718.4 5,740,464.9 2,241,348.5 7,562,754.8 5,241,949.3 
2013 214,766.2 8,244,453.2 4,633,962.3 5,727,302.8 2,252,347.0 7,358,832.4 5,155,216.3 
2014 208,848.9 8,175,009.6 4,541,675.8 5,654,876.7 2,230,490.3 7,116,046.5 5,076,965.6 
2015 202,586.4 8,095,096.0 4,483,081.4 5,484,966.3 2,227,546.9 6,786,520.4 4,836,970.6 
Region
 
Year Shanghai Jiangsu Zhejiang Anhui Jiangxi Hubei Hunan 
1996 1,103,386.6 8,540,021.0 4,640,474.1 6,480,509.1 2,820,934.9 6,217,680.9 4,398,281.2 
1997 394,994.8 8,778,633.0 4464,999.8 5,601,908.6 2,761,413.2 6,787,656.5 4,314,551.4 
1998 445,855.1 9,050,316.1 4,318,126.2 5,714,030.2 2,548,464.3 7,418,563.6 4,406,228.9 
1999 643,993.4 8,877,240.3 4,427,406.7 5,619,780.0 2,567,081.4 6,026,241.6 4,381,528.0 
2000 600,459.5 8,813,935.7 4,453,806.3 5,538,325.8 2,256,888.8 6,150,546.1 4,390,512.3 
2001 594,424.7 8,757,034.5 4,611,358.0 5,974,051.5 2,285,534.8 5,930,480.7 4,440,167.3 
2002 500,030.1 8,685,949.0 4,594,421.3 5,623,302.4 2,247,298.7 6,186,149.3 4,402,041.9 
2003 395,099.9 8,579,249.3 4,481,302.3 5,716,690.6 2,160,861.0 6,344,999.6 4,438,791.4 
2004 384,585.7 8,583,189.9 4,674,776.8 5,384,721.3 2,312,481.6 6,597,709.2 4,735,019.3 
2005 362,699.4 8,598,156.5 4,716,999.9 5,420,641.7 2,349,979.5 6,570,094.6 4,854,024.9 
2006 354,678.6 8,619,766.1 4,725,490.5 5,507,446.8 2,397,096.9 6,548,582.1 4,898,091.6 
2007 336,627.3 8,612,209.8 4,706,793.6 5,516,768.8 2,266,831.6 6,711,557.6 4,942,468.1 
2008 357,910.9 8,567,932.6 4,575,887.0 5,537,078.1 2,234,177.6 7,053,974.2 4,925,408.4 
2009 258,588.5 8,712,547.3 4,553,966.7 5,568,153.1 2,219,596.9 7,240,956.3 5,037,567.9 
2010 252,507.5 8,694,639.8 4,624,583.2 5,608,471.7 2,223,354.7 7,409,408.2 5,114,417.9 
2011 260,001.9 8,502,913.0 4,633,126.6 5,729,075.6 2,257,484.2 7,562,754.8 5,215,651.6 
2012 218,617.0 8,344,685.5 4,632,718.4 5,740,464.9 2,241,348.5 7,562,754.8 5,241,949.3 
2013 214,766.2 8,244,453.2 4,633,962.3 5,727,302.8 2,252,347.0 7,358,832.4 5,155,216.3 
2014 208,848.9 8,175,009.6 4,541,675.8 5,654,876.7 2,230,490.3 7,116,046.5 5,076,965.6 
2015 202,586.4 8,095,096.0 4,483,081.4 5,484,966.3 2,227,546.9 6,786,520.4 4,836,970.6 
Fig. 9.

Accumulation of carbon dioxide emissions from agricultural production in the study area (1996–2015).

Fig. 9.

Accumulation of carbon dioxide emissions from agricultural production in the study area (1996–2015).

Discussion

Comparison with other studies

The considerable concerns over the risks of future resource demand soaring and inefficient utilisation (in terms of regional agricultural production) highlight the urgent need to evaluate the potential impacts of APE on the WEF nexus. The amount and extent of the research documented in the introductory section on assessing the WEF nexus driving various qualitative and quantitative methods reflects the considered importance of this topic (see Table 5). As previous WEF nexus evaluations have mainly focused on the wider field of consumption–production, there are only a few studies on the WEF nexus in agricultural production.

Table 5.

Documented assessment approaches used to study the WEF nexus.

Methods Period Study area Object Literature 
Systems thinking Mekong region Resource nexus Foran (2015)  
Particular framing of global resource scarcity Global Economic growth Allouche et al. (2015)  
VMod (a distributed hydrological model) Mekong River Basin Multipurpose reservoirs Räsänen et al. (2015)  
DEA model 2005–2014 China Input output index system Li et al. (2016)  
Hydro-economic water system model Brahmaputra River Basin in South Asia Transboundary water management Yang et al. (2016b)  
Integrated framework of science and policy Resource security Al-Saidi & Elagib (2017)  
Pooled least squares regression, pooled fixed effects and pooled random effects regression techniques 1980–2013 Sub-Saharan African countries Agricultural sustainability Ozturk (2017)  
Three-stage data envelopment analysis model evaluation method 1996–2015 The middle and lower reaches of the Yangtze River, China Agricultural production efficiency This study 
Methods Period Study area Object Literature 
Systems thinking Mekong region Resource nexus Foran (2015)  
Particular framing of global resource scarcity Global Economic growth Allouche et al. (2015)  
VMod (a distributed hydrological model) Mekong River Basin Multipurpose reservoirs Räsänen et al. (2015)  
DEA model 2005–2014 China Input output index system Li et al. (2016)  
Hydro-economic water system model Brahmaputra River Basin in South Asia Transboundary water management Yang et al. (2016b)  
Integrated framework of science and policy Resource security Al-Saidi & Elagib (2017)  
Pooled least squares regression, pooled fixed effects and pooled random effects regression techniques 1980–2013 Sub-Saharan African countries Agricultural sustainability Ozturk (2017)  
Three-stage data envelopment analysis model evaluation method 1996–2015 The middle and lower reaches of the Yangtze River, China Agricultural production efficiency This study 

Foran (2015), Allouche et al. (2015) and Al-Saidi & Elagib (2017) explored the WEF nexus using qualitative evaluation methods, as Endo et al. (2015b) classified. These qualitative methods are generally used to describe the nexus in the region of interest, and include primary research methods such as questionnaire surveys, as well as secondary research methods such as ontology engineering and integrated maps. By contrast, Räsänen et al. (2015), Li et al. (2016), Yang et al. (2016b) and Ozturk (2017) adopted quantitative methods, which include physical models, benefit-cost analysis (BCA) and integrated indices. Thus, their methods brought similar results to those in this article. Like us, Li et al. (2016) built an input–output index system at the city level and evaluated the WEF nexus input–output efficiency with a DEA model. However, evaluations of production efficiency are plagued with the inevitable disturbances from within the DEA model (Dyckhoff & Allen, 2001) because of the internal mismanagement of the model and disturbances from external environmental variables (Chen et al., 2014; Zhang et al., 2017), making it hard to get a reliable APE response to be used for decision-making concerning the WEF nexus. Thus, in this study, by using a state-of-art three-stage evaluation method, we eliminated the internal mismanagement disturbances and external environmental variables from our DEA model, and constructed a more reliable assessment framework for production efficiency and optimal resource input behaviours in response to the WEF nexus at typical sites (i.e., Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei and Hunan) in China. The inter-comparison of efficiency consistencies (overall efficiency, scale efficiency and technical efficiency) made by DEA is illustrated by the productive frontier surface diagrams (Figure 4) which show that the three-stage DEA modelling evaluation method yields optimal efficiency by comparing the degree of overlap. For all six agricultural resources input into the DEA model, the three-stage evaluation method provides an effective means to describe optimal resource inputs in the past. Furthermore, results of analysis of the variation of production efficiency based on the Malmquist index indicate that the three-stage DEA modelling evaluation method is reliable and robust.

Impacts of the WEF nexus on the external environment

Driving the DEA model by merging historical agricultural and environmental data, important indexes of production efficiency, optimal resource inputs and agricultural wastes were all quantified. For most resources, the emissions of waste gases and waste water are proportional to the consumptions of resources. However, emissions of waste gases began a downward trend from 2010, though waste water showed an increasing trend. This appears to be owing to the following three points: first, derivatives of fossil fuels have been improved, thus their chemical reactions are more constant and combustion is more complete; secondly, biomass fuels are extensively replacing traditional fuel; and thirdly, centralised and scientific agricultural production patterns are changing the efficiency of fossil fuel. These measures contribute to the reduction in waste gas emissions. Nevertheless, because of agricultural non-point pollution, which is an enormous challenge, the rise in waste water continues in this region. For the study regions, the amount of carbon dioxide was quantified by transforming them into standard coal. This partly demonstrates that agricultural production impacts the region's greenhouse effect (Wang et al., 2012b; Zhang et al., 2015). This inference can be further confirmed by the increase in regional greenhouse effect in all agricultural production processes without exception. Clearly, boosting production efficiency cannot completely compensate for the augmented greenhouse effect due to fossil fuel consumption but the interactive impact of CO2 and production efficiency on field-grown grain is complicated and remains uncertain due to the impact of photosynthesis (Konzmann et al., 2013; Adachi et al., 2014) and climate change (Beck & Walker, 2013; Wang et al., 2016).

Limitations and potential future studies

This study uses a three-stage DEA modelling evaluation method to quantify the impact of the WEF nexus on agricultural production via APE analysis. The methodology is applicable nationwide as long as appropriate data are available, with the aim of providing predictions to inform APE policy dimensions driving. However, most studies so far mainly focus on coupled models which usually pay attention to the integration of hydrology, agriculture, economy and policy, lacking analytical models involving hydrological, energy, crop and environmental issues at various scales.

Consequently, relevant research into three areas could offer an in-depth understanding of the WEF nexus, comprehensively and systematically.

The first concerns multi-scale analytical methods coupled with hydrological, energy and crop models. Because the three resources are inextricably linked, constructing a multi-scale analytical methods coupled with hydrological, energy and crop models is critical for dealing with the security challenges which jointly address the WEF nexus. Similar research has already been conducted, e.g., by Räsänen et al. (2015), Yang et al. (2016a) and Yang et al. (2016b), who analysed the WEF nexus based on the large river basins in Asia using corresponding coupling models which integrated hydraulic, agricultural and economic models. However, most research so far has mainly focused on a multi-model at basin and nation level; moreover, these models usually pay attention to the integration of hydrology, agriculture, economy and policy, lacking analytical models involving hydrological, energy, crop and environmental issues at a global scale.

The second area concerns the WEF nexus in the context of climate change. A consensus has been reached by many researchers that climate change is likely to aggravate various influences on the WEF nexus (e.g., Piao et al., 2010; Waughray, 2011; Wang et al., 2012c; Beck & Walker, 2013; Ringler et al., 2013; Sahin et al., 2014; Conway et al., 2015; Smidt et al., 2016; Yang et al., 2016b; Al-Saidi & Elagib, 2017; Ozturk, 2017). As well as soaring consumption, the WEF nexus is vulnerable to (and easily exposed to) climate change. Moreover, from the holistic perspective, the climatic factors that affect the WEF nexus are numerous. Wang et al. (2007) measured soil moisture and the evaporative fraction; Choi & Qu (2017) combined satellite telemetry with observational data from the ground using microwave remote sensing technology, which could be greatly significant and potentially applied to the WEF nexus in future research.

The third area of research concerns an early warning system for climate risk. Climate change is a major threat to resource management for the WEF nexus, and acquiring timely results for early warning and to minimise effects is crucial to formulate strategies and instigate measures. Haro-Monteagudo et al. (2017) constructed a climate risk early warning system (CREWS) and utilised this monitoring system to characterise and assess climate risk.

In general, the WEF nexus should not become a fixed concept. Rather, in the future, the positioning and development of this nexus could be further explored to drive the forces of this paradigm and how they interact at different scales (Al-Saidi & Elagib, 2017).

Conclusion

This study adopted a three-stage DEA modelling evaluation method to measure and comparatively analyse APE and its influences on the WEF-nexus, as well as on the external environment for seven provinces in the MLYR from 1996 to 2015. Its primary conclusions are as follows:

  1. By eliminating the impacts of both the external environment and statistical noise on efficiency measurement, the three-stage DEA modelling evaluation method reveals the real APE and is considered a better quantitative method than conventional approaches.

  2. APE can be broken down into scale efficiency, technical efficiency and overall efficiency, scale efficiency is the dominant factor.

  3. Technological innovation, economic development level and agricultural structure have significant effects on regional APE, and are positive and significant influencing factors for the environment and for climate change.

  4. APE across the whole region concerned has decreased by 2.56% in the past two decades.

  5. The gradually widening range of APE is an important challenge for this region (e.g., the best province, Jiangsu, has an APE 1.2 times higher than the worst, Shanghai).

  6. Significantly, this region generates huge demands for agricultural resources, including 115,318 million m3 of irrigation water, 4,554.32 mm of effective rainfall, 888,089 million kilowatt-hours of agricultural electricity, 20,584,400 tons of agricultural fertiliser, 5,674,200 tons of agricultural diesel fuel and 687,500 tons of agricultural pesticide.

  7. The waste gas emissions from agricultural production processes are mainly SOx, NOx, CO, HC and CO2.

  8. The regional emissions of GHG gases decreased from 34,200,000 tons standard coal in 1996 to 32,110,000 tons standard coal in 2015. Our results highlight their significance in relation to national GHG emissions and underline the importance of water and energy resource use for food production.

Acknowledgments

This work was jointly supported by the National Science Foundation of China (51779073, 51609065, 51509001), the Australian Research Council (Discovery Project Grant DP170104138), the Fundamental Research Funds for the Central Universities (2017B21414), the National ‘Ten Thousand Program’ Youth Talent, the QingLan Project, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Social Science Fund of Jiangsu Province (17GLC013). Thanks to the National Meteorological Information Center, China Meteorological Administration (http://cdc.cma.gov.cn/) for offering the meteorological data; thanks to the World Bank (http://data.worldbank.org/) for offering the energy data; and thanks also to the Ministry of Environmental Protection of the People's Republic of China for offering the corresponding environmental data (http://www.mep.gov.cn/). Cordial thanks are extended to Dr Jerome Delli Priscoli (Editor-in-Chief) and two anonymous referees for their valuable comments which greatly improved the quality of this paper.

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