Study on Regional Differences and Convergence of Green Development Efficiency of the Chemical Industry in the Yangtze River Economic Belt Based on Grey Water Footprint

Grey water footprint is included in the green development efficiency evaluation index system of the chemical industry. From 2002 to 2016, the super efficiency Slack Based Measure (SBM) model was used to measure the green development efficiency of the chemical industry in the Yangtze River Economic Belt. Dagum Gini coefficient and its decomposition method were used to decompose the regional differences of green development efficiency of the chemical industry in the Economic Belt, and the coefficient of variation method and panel data regression model were used to test the convergence characteristics. The following results were obtained. (1) The total grey water footprint of the chemical industry in the Yangtze River Economic Belt showed a fluctuating downward trend from 2002 to 2016. (2) The green development efficiency of the chemical industry in the Yangtze River Economic Belt was significantly improved, and the spatial differentiation law of gradient decline in the upper, middle, and lower reaches of the Economic Belt was shown. (3) The regional difference of green development efficiency of the chemical industry in the Yangtze River Economic Belt initially showed an expanding trend and then a narrowing trend. Regional differences in the upper reaches of the Yangtze River increased while those in the middle reaches first increased and then decreased, whereas those in the lower reaches decreased significantly. The variance in green development efficiency of the chemical industry is the main cause of regional differences. (4) From 2012 to 2016, the Yangtze River Economic Belt had obvious convergence in its whole region, middle reaches, and lower reaches and an inconspicuous convergence in the upstream area. Regional difference of green development efficiency of the chemical industry in the Economic Belt was the combined effect of the results of environmental regulation, industrial structure, foreign investment intensity, and scientific and technological advancements. Our results have high theoretical reference values and practical guiding significance for implementing the green efficiency promotion strategy of the chemical industry in Yangtze River Economic Belt by region and classification.

Introduction
The Yangtze River Economic Belt is one of the most critical contradiction areas between economic development and environmental protection in China [1]. A total 40% of available freshwater resources and more than 20% of its wetland resources in China are concentrated in the Yangtze River Basin, which covers 204 national aquatic germplasm resources protection zones. The River Basin is one of the important ecological security barriers and economic centers in China [2]. The chemical industry is a basic and pillar industry of the national economy, with high dependence on water and energy as well as high safety and environmental risks [3]. China is the largest chemical producer in the world. In 2018, its chemical turnover was 119.8 million EUR, accounting for 35.8% of the global chemical sales for the year [4]. The production value of chemical products in the Yangtze River Economic Belt accounts for more than 40% of the country's total. At present, "chemical industry encircling the river" poses challenges to the Yangtze River Economic Belt. There are more than 400,000 chemical enterprises, 5 steel bases, 7 oil refineries, and many large petrochemical bases along the Yangtze River, leading to a greater risk of environmental pollution [5]. In the "joint efforts to protect" and under the general requirements of "no large-scale development", the green transformation and development of the chemical industry in the Yangtze River Economic Belt is particularly urgent.
The Chinese economy has entered a high-quality development stage from the highspeed growth stage now. The traditional industrial development mode of high energy consumption, high pollution, and high emission has gradually changed to the intensive, efficient, and sustainable green development mode. The Chinese government has attached great importance to the green development of the chemical industry in the Yangtze River Economic Belt. On 14 November 2020, General Secretary Xi Jinping presided over a forum that aimed to promote the comprehensive development of the Yangtze River Economic Belt, stressing the need to make it the main battlefield of Chinese ecological priority and green development. Relevant departments of the state have also issued a series of policies and regulations, such as guiding opinions on strengthening the green development of industries in the Economic Belt, the ecological environment protection plan for the area, the Law of the People's Republic of China on the Protection of the Yangtze River, and so forth. All of these endeavors actively promote the green transformation and upgrading of the chemical industry in the Yangtze River Economic Belt. In the past five years, more than 8000 chemical enterprises along the Economic Belt have been reformed, relocated, transformed, or closed. Remarkable achievements have been made in the green transformation and development of the chemical industry. The ecological environment has been significantly improved. The proportion of excellent water quality sections in the Yangtze River basin increased from 82.3% in 2016 to 91.7% in 2019 and further increased to 96.3% from January to November 2020. The elimination of poor V water bodies achieved for the first time in 2020. It can be seen that comprehensively promoting the green development of the chemical industry and improving its green development efficiency [6] are key to solving the dilemma of the "chemical industry surrounding the river", which ensures environmental and industrial development safety and realizes the sustainable development of the chemical industry. The Yangtze River Economic Belt includes 9 provinces, 2 cities, and 11 provincial administrative units and covers an area of approximately 2.05 million km 2 [7]. Due to the differences in resource conditions, economic development levels, innovation abilities, and chemical industry development histories, the spatial distribution and green development level of the chemical industry show spatial heterogeneity, which increases the challenge for the Economic Belt to promote the industrial green development. In order to measure the gray water footprint and green development efficiency of the chemical industry in the Yangtze River Economic Belt, to reveal the spatial differences and their convergence in the green development efficiency of the chemical industry in 11 provinces and cities, and to provide policy support for the green development of the chemical industry in the Yangtze River Economic Belt, this research systematically studied the regional differences and convergence of green development efficiency of the chemical industry in the Yangtze River Economic Belt. The results are expected to be valuable in theoretical reference and practical significance for implementing green development promotion strategy of the chemical industry in different regions and categories.

Literature Review
The concept of "green development" was first proposed by the United Nations Development Programme in 2002. The essence of green development is to regard resources and the environment as endogenous factors of growth and provide a balance between economic growth and ecological environment protection by changing the dynamic mechanism of economic development to form a new sustainable development model [8,9]. From the perspective of input and output, green development efficiency refers to the proportional relationship between green development output and input. Green development efficiency is an important indicator to analyze the degree of green development of industries and is often used to reflect the completion degree, achievements, and effectiveness of green development. Since data envelopment analysis (DEA) can consider a variety of input and output and does not need to set specific function forms, it has become the mainstream method to measure green development efficiency [10]. Pittman (1983) first included "undesirable" output into the productivity analysis process [11]. Chung et al. (1997) proposed directional distance function and the Malmquist-Luenberger Index (MLI), which carries out productivity evaluation after the "undesirable" output is considered reasonable [12]. Tone (2001Tone ( , 2002 proposed a SBM model that considers relaxation measures to effectively overcome radial and angular defects [13,14]. A DEA analysis method based on the measurement of slack variables, which puts the input and output slack directly into the objective function so that it can directly measure the inefficiency caused by slack compared to the optimal production frontier, thus solving the problem of input and output slack in the traditional DEA model, removing the inefficiency caused by slack, and also solving the problem of productivity evaluation in the presence of non-expected outputs. Many scholars used the DEA model to discuss the green development efficiency of the chemical industry. Tanzil and Beloff (2006) summarized the sustainability indicators and indicators of the chemical industry, focusing on ecological efficiency and company-specific indicators [15]. Alessandro et al.
(2017) measured the environmental economic efficiency of Italian and German chemical enterprises [16]. Yeh Jiahuey et al. (2019) calculated the total factor green energy efficiency of China's chemical industry [17]. Yijun Zhang et al. (2020) used the three-stage SBM-DEA model and MLI to measure the green total factor productivity (GTFP) of China's chemical industry [6]. Sun Honghai (2017) used super-efficiency DEA to calculate the ecological efficiency of 25 petrochemical enterprises in China [18]. Yuan Yaqiong (2018) used DEA and value-driven analysis to evaluate the ecological efficiency of heavy chemical enterprises in Beijing, Tianjin, and Hebei region from 2012 to 2016 [19]. Lu Qiuqin et al. (2020) used the improved three-stage DEA model to evaluate the transformation and upgrading efficiency of China's coal chemical enterprises [20].
When using DEA, researchers usually take labor, capital, and energy as inputs, the output value of the chemical industry as the expected output, and environmental pollutants as the unexpected output to build an evaluation model of green development efficiency of the chemical industry. These indicators do not consider the characteristics of the chemical industry, which has a great impact on water environment. Tony Allan proposed "Virtual Water"; Hoekstra et al. proposed the concept of "Water Footprint". Grey water footprint refers to the volume of freshwater required to dilute certain pollutants on the basis of existing water quality standards and natural background concentration [21]. Given that water footprint and grey water footprint can better represent the water consumption and water pollution accounting of industries [22], they have been gradually incorporated into the evaluation framework of the green development efficiency of regional industries [23,24].
The spatial distribution and environmental risk of the chemical industry in the Yangtze River Economic Belt have always been hot areas of academic concern. For a long time, the spatial layout of the chemical industry in the Economic Belt has reflected two major factors: the proximity to raw materials and market. The chemical industry along the Yangtze River is mainly distributed in the areas of Shanghai and Jiangsu [25]. In recent years, the petrochemical industry had a trend of expansion along the river to the upstream. The environmental pollution load gradient also shifted to the middle and upper reaches, and the environmental risk increased [26,27]. Xiang et al. (2021) found that the spatial differentiation characteristics of green development efficiency of the chemical industry in the Yangtze River Economic Belt were obvious. Economic level, scientific and technological innovation, industrial structure, and industrial agglomeration are the main factors affecting the spatial differentiation of green development efficiency of the chemical industry in the Economic Belt. The impact of foreign investment intensity and environmental regulation is relatively weak [28]. Therefore, it is necessary to guide the chemical industry of the Yangtze River Economic Belt to gather in large coastal bases and raw material producing or consumption areas, improve the rate of chemical enterprises entering the park, and optimize the spatial layout of the chemical industry [29]. Some scholars also studied the negative effects of the development of the chemical industry on the ecological environment. Zhu Deming et al. (2006) showed that the development of the chemical industry along the Yangtze River in Jiangsu threatened the drinking water source and water supply safety [30]. Intensive chemical enterprises and unreasonable industrial layout along the Yangtze River Economic Belt have brought some potential environmental risks to the environmental protection of the Yangtze River Basin [31,32]. Dong et al. (2020) found that the division level of heavy chemical industry in the middle and upper reaches of the Yangtze River Economic Belt decreased, which promoted the decline of the regional pollution level [33].
The contribution of this research is mainly reflected in the following aspects. Firstly, it makes up for the industry characteristics that little considered the impact of the chemical industry on the water environment in previous studies. In this study, water footprint and grey water footprint are included in the green development efficiency measurement index system of the chemical industry, and the green development efficiency of the chemical industry is established by using DEA, which was calculated from 2002 to 2016 in the Yangtze River Economic Belt. Secondly, Dagum Gini coefficient and its decomposition method are used to decompose the regional differences of green development efficiency of the chemical industry in the Economic Belt. Lastly, the convergence characteristics of green development efficiency of the chemical industry in the Economic Belt and its upstream, middle, and lower reaches are tested with the coefficient of variation method and panel data regression model from three aspects, i.e., convergence, absolute convergence, and conditional absolute convergence.

Regional Overview
The Yangtze River Economic Belt consists of the 11 provincial administrative units of Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Guizhou, Chongqing, Sichuan, and Yunnan ( Figure 1) and covers an area of about 2.05 million km 2 , accounting for 21% of the country and more than 40% of the total population and economy [7]. It is one of the Chinese chemical industry agglomeration areas. In 2016, 11 provinces and cities in the Yangtze River Economic Belt achieved a total sales value of 8253.40 billion RMB, accounting for 43.46% of the total sales value in China. The sales output values of the chemical industry in downstream areas, middle reaches, and upstream area were 530.18 billion RMB, 1816.96 billion RMB, and 1134.663 billion RMB, accounting, respectively, for 27.91%, 9.57%, and 5.97% of that in China. The sales value of the chemical industry in Jiangsu province was the highest at 2954.89 billion RMB, about 15.56% of the whole country, while that in Yunnan province was the lowest at 139.00 billion RMB, accounting for 0.73% of that in the whole country [28].

Calculation of Grey Water Footprint of the Chemical Industry
Industrial wastewater is directly discharged into surface water. The main pollutants in industrial wastewater can be measured directly, such as chemical oxygen demand (COD) and ammonia nitrogen (NH + 4 -N) in chemical industry wastewater. Therefore, COD and NH + 4 -N are used as the main indicators to measure the grey water footprint of the chemical industry. The calculation formula is as follows [23]: where GWF ind (billion m 3 ) is the grey water footprint of the chemical industry, GWF ind(i) (billion m 3 ) is the grey water footprint of the chemical industry with the standard of category i pollutants, W ed (billion m 3 ) is the discharge amount of chemical industry wastewater, and GWF reg (billion m 3 ) is the grey water footprint of the regional chemical industry. China's Standard Limits for Basic Items of Surface Water Environmental Quality Standard (GB 3838-2002) is used as the standard. In the standard, the water quality is required to meet the class III water quality index, and the concentration limits of COD and ammonia nitrogen (NH + 4 -N) in class III water are taken as the environmental concentration standards of COD and ammonia nitrogen (NH + 4 -N) in water.

Measurement Model of Green Development Efficiency of the Chemical Industry
The SBM-undesirable model was proposed to measure the green development efficiency of the chemical industry. It is calculated as [34]: Supposing there are n individual DMUs, including input vector, expected output, and unexpected output, respectively, that are recorded as x, x ∈ R m , y g ∈ R s1 , and y b ∈ R s2 . The matrix is defined as According to the actual input and output, supposing x i > 0, y g i > 0, y b i > 0, productive collection P, that is, N element input X. All combinations of expected and undesired outputs can be defined as Therefore, the SBM-undesirable model can be expressed as Type: S − i , S g r , and S b r , respectively, represent the first i 0 input redundancy, expected output deficiency, and expected output superscalar of each decision-making unit; S − i , S g r , and S b r , respectively, denote their corresponding vectors; and λ is the weight vector. The optimal solution of the above formula is (λ*, S − *, S g *, S b *). P* = 1 only when the bad output exists, that is, S − * = 0, S g * = 0, S b * = 0 when DMU 0 is efficient.

Dagum Gini Coefficient and Decomposition Method
By using the Dagum Gini coefficient method, this study analyzes the spatial differences and sources of green development efficiency of the chemical industry in the upper, middle, and lower reaches of the Yangtze River Economic Belt. According to the Gini coefficient and its subgroup decomposition method proposed by Dagum (1997), the definition of Gini coefficient G is as shown in Equation (1) [35]: where j and h are subscripts for different regions; i and r are the indexes of provinces and cities, respectively; n is the total number of provinces and cities; k is the total number of regions; and n j (n h ) and j(h) are the number of provinces and cities within a region. y ji (y hr ) is the green development efficiency of the chemical industry in j(h) regional provinces and cities i(r), and y is the average value of green development efficiency of the chemical industry in all provinces and cities. On the overall Gini coefficient G by region, according to the average value of green development efficiency of the chemical industry in each region k, the region is sorted and then the Gini coefficient G is divided into three parts: intraregion (intra-group) difference pairs G contribution of G w , interregional (inter-group) difference pairs G contribution of G nb , and interregional (inter-group) ultra-variable density pairs G contribution of G t . When the three meet, G = G w + G nb + G t , in which the area j has a Gini coefficient of G jj and intraregional differences G w . The calculation formulas are Formulas (2) and (3), respectively; zones j and h have a Gini coefficient between G jh and the regional net difference G nb . The calculation formulas are Formulas (4) and (5), respectively. The calculation formula for the interregional super-variable density G t is shown in Formula (6).
In Equation (5), p j = n j /n, s j = n j y j /ny, and j = 1, 2, 3. In Equation (7), D jh denotes region j and h. See Formula (7) for the relative influence of green development efficiency of the chemical industry. d jh is the difference of the green development efficiency of the chemical industry between regions (see Equation (8)). j, h all y ji − y hr > 0 is the mathematical expectation of the sample summation; p jh is the super-variable first-order moment, representing the region. j, h all y hr − y ji > 0 is the mathematical expectation of the sample summation.
where F j (F h ) represents the area j(h)C, which is the cumulative distribution function of green development efficiency of the chemical industry.

Convergence Model
To investigate the evolution trend of green development efficiency of the chemical industry in the whole Yangtze River Economic Belt and the upper, middle, and lower reaches, the convergence analysis is carried out, including σ Convergence and β Convergence.
σ Convergence refers to the trend where the deviation of green development efficiency of the chemical industry in different regions is decreasing over time. σ Convergence is measured by the coefficient of variation and can be calculated as [36]: In Equation (5), pj = nj/n, / j j j s n y ny = , and j = 1, 2, 3. In Equation (7), Djh denotes region j and h. See Formula (7) for the relative influence of green development efficiency of the chemical industry. djh is the difference of the green development efficiency of the chemical industry between regions (see Equation (8)). j, h all yji − yhr > 0 is the mathematical expectation of the sample summation; pjh is the super-variable first-order moment, representing the region. j, h all yhr − yji > 0 is the mathematical expectation of the sample summation.
jh jh where Fj(Fh) represents the area j(h)C, which is the cumulative distribution function of green development efficiency of the chemical industry.

Convergence Model
To investigate the evolution trend of green development efficiency of the chemical industry in the whole Yangtze River Economic Belt and the upper, middle, and lower reaches, the convergence analysis is carried out, including σ Convergence and β Convergence.
σ Convergence refers to the trend where the deviation of green development efficiency of the chemical industry in different regions is decreasing over time. σ Convergence is measured by the coefficient of variation and can be calculated as [36]: where j indicates the number of areas (j = 1, 2, 3…), i indicates the number of provinces and cities in the region (i = 1, 2, 3…), Nj is the number of provinces and cities in each region, and Fij denotes that the region j exists t with an average value of green development efficiency of the chemical industry in the period.
The β convergence model is [36]: The left side of the model is the growth rate of green development efficiency of the chemical industry calculated by logarithmic difference, where μi is a fixed effect, vt is a time-fixed effect, and εit is a random error term.
In condition β, the convergence model is absolute β. A series of control variables is added to the convergence model. This study adds environmental regulation, industrial where j indicates the number of areas (j = 1, 2, 3 . . . ), i indicates the number of provinces and cities in the region (i = 1, 2, 3 . . . ), N j is the number of provinces and cities in each region, and F ij denotes that the region j exists t with an average value of green development efficiency of the chemical industry in the period.
The β convergence model is [36]: The left side of the model is the growth rate of green development efficiency of the chemical industry calculated by logarithmic difference, where µ i is a fixed effect, v t is a time-fixed effect, and ε it is a random error term.
In condition β, the convergence model is absolute β. A series of control variables is added to the convergence model. This study adds environmental regulation, industrial structure, technical level, and foreign investment intensity as control variables. The convergence model for condition β is In the regression process, each variable is logarithmic. In this paper, a two-way fixed effect model is adopted to improve the coefficient. In the β accuracy of estimation, the robust error standard of clustering is adopted to the provincial and municipal levels. If β < 0 and is significant, the green development efficiency of the chemical industry in the Yangtze River Economic Belt converges, or it diverges. The rate of convergence b = −ln(1+ β)/T. According to existing research results, combined with the classification and characteristics of the chemical industry, the evaluation index system of green development efficiency of the chemical industry is constructed from input and output. Manpower, capital, energy, and water for the chemical industry are selected as investment indexes. The sales output value of the chemical industry is selected as the expected output index and the grey water footprint of the chemical industry as the unexpected output index (Table 1). Considering the availability of data of the chemical industry, the scope of the chemical industry is defined as five subsectors in the manufacturing industry by the China Industrial Statistics Yearbook: petroleum processing, coking and nuclear fuel processing; chemical raw materials and chemical products manufacturing; pharmaceutical manufacturing; chemical fiber manufacturing; and rubber and plastic products manufacturing. There are no direct statistical data of total industrial water consumption and wastewater discharge in the statistical yearbook, so we apply the data of industrial wastewater and pollutant discharge in wastewater for each subsector in China to estimate the data of pollutant discharge in industrial wastewater for each subsector in each province [37]. Energy data of the chemical industry are estimated by reference [38]. For the net fixed capital and industrial sales output value of the chemical industry, the fixed assets investment price index of corresponding provinces and cities and the ex-factory price index of industrial producers are used for price reduction, which is reduced to the level of 2000.

Variables Affecting the Efficiency of Green Development of the Chemical Industry
Using environmental regulations, industrial structure, foreign investment intensity and technological progress as control variables, this paper studied their influence on the green development efficiency of the chemical industry in the Yangtze River Economic Belt. Among them, the total amount of environmental governance for environmental regulation represents the proportion of GDP. Science and technology investment is represented by the proportion of science and technology expenditure in fiscal expenditure, which is representative of the investment amount of foreign-funded enterprises at the end of the year. The proportion of the secondary industry in GDP represents the industrial structure. In 2008, due to the impact of the financial crisis, the operation of chemical enterprises was impacted, the production capacity decreased, and the total grey water footprint was at its lowest point. After the financial crisis, thanks to the support of relevant national policies, chemical enterprises gradually eliminated the crisis, the output of the chemical industry gradually recovered, and the discharge of wastewater in the chemical industry increased, leading to an increase in the total grey water footprint of the chemical industry.
The grey water footprint of the chemical industry in Jiangsu, Zhejiang, Hubei, Hunan, Sichuan, and Yunnan provinces is relatively high. These provinces are the main concentration provinces of the chemical industry in the Economic Belt, with large-scale enterprises that have high amounts of wastewater discharge. The chemical industry in Shanghai has the lowest wastewater footprint. On the one hand, Shanghai has accelerated the adjustment of its industrial structure, the proportion of the chemical industry in the national economy has decreased, and the overall scale of the chemical industry has shrunk. In 2016, the sales value of its chemical industry only accounted for 2.67% of that in China. On the other hand, the chemical industry in Shanghai is gradually transforming and upgrading to the direction of a high-end, green, and low-carbon chemical industry. Shanghai has carried out the construction of a "Green Industrial zone" earlier in China, and its environmental and economic indicators of 10,000 CNY of output value led the national level of the same industry. The grey water footprint of the chemical industry in Guizhou is relatively low, mainly because of the small scale of the chemical industry. In 2016, the sales value of the chemical industry in Guizhou only accounted for 0.83% of the national total ( Figure 2).

Spatial and Temporal Evolution of Green Development Efficiency of the Chemical Industry in Yangtze River Economic Belt
From 2002 to 2016, the green development efficiency of the chemical industry in the Yangtze River Economic Belt showed an overall development and evolution trend of first decreasing and then increasing, with an average of 0.5163, only reaching the optimal level of 51.63% (Table 2). This trend showed that the overall level of green development efficiency of the chemical industry is not high and still has great growth potential. Note that the green development efficiency of the chemical industry showed a downward trend from 2002 to 2005, which may be due to the reversal of China's economic model in the later stage of its 11th Five-Year Plan. Moreover, the chemical industry turned back to the development model of high consumption, high pollution emission, and low efficiency. The average green development efficiency of the chemical industry in the Yangtze River Economic Belt increased significantly during 2012 and 2016, which is the reason that provinces and cities in the Economic Belt accelerated the green development, transformation, and upgrading of the chemical industry and achieved remarkable results after the 18th National Congress.  From the upstream, midstream, and downstream areas, the average green development efficiency from 2002 to 2016 of the chemical industry in the downstream area was 0.8343, which was in a high-level development state with a small overall change range. The average green efficiencies of the chemical industry in the midstream and upstream areas were 0.4156 and 0.2739, respectively, which are relatively low and generally show an evolutionary trend of first declining and then rising ( Table 2).
In terms of provinces and cities, the green development efficiency of the chemical industry in Shanghai, Zhejiang, and Jiangsu has been maintained at the optimal state of 1.00 (except when it was at 0.79 in 2005), while that in Hunan province also reached the optimal state of 1.00 from 2012 to 2016. The green development efficiency of the chemical industry in Anhui, Hubei, Chongqing, Sichuan, Guizhou, and Yunnan provinces increased to varying degrees, showing a development trend of first decreasing and then increasing. However, there is still a large gap in the green development efficiency of the chemical industry between these provinces and the Shanghai, Zhejiang, and Jiangsu provinces (Figure 3).

Regional Difference Analysis of Green Development Efficiency of the Chemical Industry in the Yangtze River Economic Belt
To further reveal the regional differences and sources of green development efficiency of the chemical industry in the Yangtze River Economic Belt, Dagum Gini coefficient and its decomposition method were used to calculate and decompose its relative level.

Overall Regional Differences
From 2002 to 2016, the average regional difference of green development efficiency of the chemical industry in the Yangtze River Economic Belt was 0.3080, showing a development trend of first expanding and then narrowing. The maximum and minimum regional differences of green development efficiency of the chemical industry appeared in 2007 and 2016 at 0.3347 and 0.2577, respectively. From 2007 to 2010, the green development efficiency of the chemical industry fluctuated greatly, due mainly to the impact of the financial crisis and the inconsistent degree of recovery of such efficiency in various provinces and cities. From 2012 to 2016, the regional differences in green development efficiency of the chemical industry narrowed, mainly because since the 18th National Congress of the Communist Party of China, the middle and upper reaches with low green development efficiency of the chemical industry have strengthened the treatment of the chemical industry, improved its resource and energy utilization efficiency, reduced waste water discharge, and improved the green development efficiency, thereby reducing the regional differences in green development efficiency of the chemical industry.

Intraregional Differences
On the whole, the regional difference of green development efficiency of the chemical industry in downstream areas is the largest, with an average of 0.1472. The second is the middle reaches, with an average of 0.1043. The upstream area is the smallest, with an average of 0.0873. From the evolution trend, from 2002 to 2012, the regional difference of green development efficiency of the chemical industry in the middle and lower reaches showed an upward trend in fluctuation. From 2012 to 2016, it showed a downward trend. From 2002 to 2016, the green development efficiency of the chemical industry in the upstream region showed an upward trend in fluctuation, indicating that the regional differences are expanding. Note that, although the regional differences of green development efficiency of the chemical industry in the upstream region is the smallest, they are expanding. Hence, it is necessary to strengthen the regulation of regional difference of green development efficiency of the chemical industry in the upstream region. Although there are great regional differences in the green development efficiency of the chemical industry in the middle reaches and downstream areas, these have narrowed significantly since 2012.

Differences between Regions
From the mean value of green development efficiency of the chemical industry among the three regions, the regional difference between the downstream and upstream areas is the largest at 0.5081, between the lower and middle reaches is 0.3805, and between the middle reaches and the upper reaches is the smallest at 0.1872. From the changing trend, the regional difference between the middle and upper reaches tends to expand, whereas those between downstream and upstream and between downstream and midstream tend to narrow (Figure 4).

Source of Difference
From the perspective of difference sources, the contribution of inter-group differences is the largest with an average value of 0.2545, which is higher than that of regional differences with an average value of 0.0453, and the contribution of over variable density with an average value of 0.0082. The evolution trend of inter-group differences is similar to that of overall regional differences, and the contribution rate of the average value of inter-group differences is as high as 78.94%. This percentage showed that the difference between groups is the main factor affecting the overall regional difference of green development efficiency of the chemical industry in the Yangtze River Economic Belt. The contribution rates of intra-group difference and hypervariable density were 14.05% and 2.54%, respectively, which have relatively small contributions to the overall regional difference (Table 3).

σ-Convergence Test
The σ-convergence test method is used to calculate the σ-convergence coefficient of the whole Yangtze River Economic Belt and the upper, middle, and lower reaches, as shown in Figure 5. From 2002 to 2004, the global σ-convergence coefficient of the Economic Belt increased and showed a divergent state. From 2006 to 2016, the global σ-convergence coefficient generally showed a downward trend, indicating that the global σ-convergence occurred. This means that the regional differences in the green development efficiency of the chemical industry in the Yangtze River Economic Belt are shrinking.  From different regions, the σ-convergence coefficient of downstream areas showed a downward trend from 2002 to 2016 and σ-convergence, indicating that the regional difference in green development efficiency of the chemical industry in downstream areas had narrowed. From 2002 to 2012, the σ-convergence coefficient in the middle reaches increased and then decreased, and after 2012, it continued to decline, showing σ-convergence. After 2012, the regional difference of green development efficiency of the chemical industry in the middle reaches was reduced. From 2002 to 2016, the σ-convergence coefficient in the upstream region basically showed an upward trend and no σ-convergence, indicating that the difference in green development efficiency of the chemical industry in the upstream region was expanding.
Overall, the green development efficiency of the chemical industry in the whole region and the downstream areas of the Economic Belt has σ-convergence. After 2012 in the middle reaches, the green development efficiency of the chemical industry also had σ-convergence. There is no σ-convergence in the upstream region, and the regional imbalance of green development efficiency of the chemical industry intensified, which is basically consistent with the analysis results of Gini coefficient.

Absolute Convergence of β
The Hausman test shows that the panel data model with time and individual double fixed effects is more appropriate, and so the β absolute convergence mechanism was tested. The results show that the β absolute convergence coefficients in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt are negative, indicating that the green development efficiency of its chemical industry exists in β absolute convergence. Among them, the significance of the whole region, the upstream region, and the middle reaches region passed the significance test of 1%, 5%, and 5%, respectively. These results showed that the growth rate of green development efficiency of the chemical industry in the whole region, the upstream region, and the middle reaches region of the Yangtze River Economic Belt converged, while the significance of the downstream region did not pass the test. In terms of convergence speed, the upstream region is the fastest, followed by the midstream region (Table 4). 0.0127% 0.0196% 0.0132% -Note: ** and ***, respectively, represent significance at the confidence levels of 5% and 1%, and T statistics are in brackets. "-" means empty.
Conditional β convergence does not require different regions to have the same basic characteristics, i.e., different regions can be at different growth paths and steady-state levels. If conditional β convergence exists, they eventually converge to their respective steady states by virtue of their own characteristics. In this paper, the green development efficiency of chemical industry in the Yangtze River Economic Belt is examined in four aspects, namely environmental regulation, industrial structure, foreign investment intensity, and scientific and technological progress, to investigate which factors contribute to the green development efficiency of chemical industry in Yangtze River Economic Belt to reach the conditional convergence. After controlling the control variables, such as environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress, the β absolute convergence coefficient in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt was still negative. In addition, the significance of the whole region and the middle reaches passed the significance test at 1% and 5%, respectively. This shows that the green development efficiency of the abovementioned regional chemical industry follows the trend of β absolute convergence, which is under the consideration of environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress. In terms of convergence rate, the convergence rate in the middle reaches is faster.
In the panel data regression model of the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt, the regression coefficients of the control variable environmental regulation are negative, and the whole region and the upper reaches pass the 5% and 10% significance tests, respectively. This finding showed that the environmental regulation of the whole region and the upper reaches restricts the reduction in regional difference in the green development efficiency of the chemical industry. The regression coefficients of industrial structure in the whole region, the middle reaches, and the downstream regions are positive, while those of the upstream regions are negative, but they all fail to pass the significance test. The regression coefficient of foreign capital intensity in the whole region, the upstream region, and the downstream region is positive, while that of the middle reach region is negative, but only the upstream region passes the significance test. The results showed that the foreign capital intensity helps reduce the regional difference of green development efficiency of the chemical industry in the upstream region. The regression coefficients of scientific and technological progress in the whole region, upstream, midstream, and downstream regions are positive, but only the whole region passes the significance test. The outcome means that the improvement of scientific and technological level helps reduces the difference in green development efficiency of the chemical industry in these regions but is not the main reason (Table 5). Note: *, **, and ***, respectively, represent significance at the confidence levels of 10%, 5%, and 1%, and T statistics are in brackets. "-" means empty.

Discussion
First, the problem of water ecological environment in the Yangtze River Economic Belt has attracted increasing research attention. Grey water footprint has been widely recognized as an indicator of pollution intensity [39,40]. The industrial grey water footprint index can better reflect the water pollution of industrial production activities than the wastewater pollutant discharge index can [23]. In this study, grey water footprint is incorporated into the evaluation framework of green development efficiency of the chemical industry. It can better reflect the impact of chemical industry production activities on the water environment and provide new research ideas for the calculation of green development efficiency of the chemical industry. In recent years, the green development, transformation, and upgrading of the chemical industry in the Economic Belt has achieved remarkable results. Nonetheless, in the future, we should still focus on the dynamic change trend of pollutant discharge in chemical industry wastewater, further strengthen the water pollution control of the chemical industry, optimize the industrial scale, reduce the grey water footprint of the chemical industry, and improve the efficiency of grey water footprint of the chemical industry. On the basis of this calculation model, provinces and cities can also build a measurement model of green development efficiency of the chemical industry based on grey water footprint, monitor the green development of the chemical industry, and put forward governance strategies. However, the water consumption and grey water footprint data of the chemical industry used in this paper were estimated using the provincial and industrial data in the Yearbook of China Economic Census (2008) and reference [38], and there is a certain error with the actual value. In future research, it is still necessary to improve the accuracy of data estimation or expand the channels for obtaining water environment data of the chemical industry.
Second, this study showed that the green development efficiency of the chemical industry in the Yangtze River Economic Belt increased significantly from 2002 to 2016 and showed a development and evolution trend of first declining and then rising. Yijun Zhang (2020) found that the overall green development performance of China's chemical industry showed a significant improvement trend from 2007 to 2017 [6]. Yeh Jiahuey (2019) studied the green development performance of China's chemical industry from 1980 to 2013 and found that it showed an evolution law of first declining and then rising from 2002 to 2013 [17]. These results are basically consistent with the results of the present research. In recent years, the Chinese government has strengthened the construction of ecological civilization and actively promoted the green transformation and upgrading of the chemical industry with remarkable results. In particular, the 18th Congress of the Communist Party of China in 2012 proposed to give prominence to the construction of ecological civilization and build a "Beautiful China." According to the data of the China Development and Reform Commission from 2016 to 2020, there were more than 8000 chemical enterprises along the Yangtze River. Furthermore, the proportion of excellent water environment sections in the Yangtze River Basin increased from 82.3% in 2016 to 91.7% in 2019 and further increased to 96.3% from January to November in 2020, and the proportion of inferior class V water quality in the Yangtze River Basin decreased from 3.5% to 0.6% during 2016 and 2019. The elimination of inferior class V water bodies would be realized for the first time in 2020, which showed that the green development of the chemical industry in the Economic Belt has achieved remarkable results, thus supporting the conclusions of this paper from the practical level.
Third, the regional heterogeneity of green development efficiency of the chemical industry in the Yangtze River Economic Belt is very obvious. Affected by the natural geographical environment and socioeconomic conditions, there are obvious gaps in the socioeconomic development level, industrialization level, and scientific and technological innovation ability in the upper, middle, and lower reaches of the Yangtze River Economic Belt. The zonality of industrial ecological efficiency [41] and green development level [42] is significant. This study found that the green development efficiency of the chemical industry in the Yangtze River Economic Belt was the highest in the lower reaches from 2002 to 2016, followed by the middle reaches, and the lowest in the upper reaches. The overall regional difference and interregional difference tended to narrow, and the intraregional difference expanded. Our result is similar to the research conclusions of Yunbo Xiang (2021) on the spatial differences of green development efficiency of the chemical industry in the Yangtze River Economic Belt [28], but there are some differences in specific values, which may be caused by different measurement indicators and used models. According to the above research results, in the future, the focus of the chemical industry governance in the Yangtze River Economic Belt would still be to accelerate the green development, transformation, and upgrading of the chemical industry in the middle and upper reaches, improve the green development efficiency of the chemical industry, and reduce regional differences. At the same time, we should pay attention to controlling regional differences and preventing their expansion. The middle and upper reaches should improve the green development efficiency of the chemical industry by means of technological innovation, optimizing the industrial scale, adjusting industrial structure, and strengthening environmental regulation. At the same time, we should promote the diffusion of technology, capital, and talents in the lower reaches to the middle and upper reaches. We should promote the green and coordinated development of the chemical industry in the Yangtze River Economic Belt.

Conclusions
This research studied the regional differences, influencing factors, and convergence of green development efficiency of the chemical industry in the Yangtze River Economic Belt by using the super efficiency SBM model, Dagum Gini coefficient, coefficient of variation method, and panel data regression model. The green development efficiency measurement model of the chemical industry constructed in this paper can more objectively and comprehensively reflect the impact of the chemical industry on the water environment than in previous studies and is very consistent with the industrial characteristics of the chemical industry and the regional water environment problems of the Economic Belt. Accurately measuring the green development efficiency of the chemical industry in the Economic Belt can provide theoretical support for chemical water treatment and green development in the area. Analyzing the difference, influence, and convergence of green development efficiency of the chemical industry in the Yangtze River Economic Belt can provide reference for formulating strategies to improve the green development efficiency of the chemical industry by region and classification.
The main conclusions of this paper are as follows. First, from 2002 to 2016, the total grey water footprint of the chemical industry in the Yangtze River Economic Belt showed a downward trend, while the green development efficiency of the chemical industry showed an upward trend as a whole. This finding means that remarkable achievements have been made in environmental governance and green development of the chemical industry in the Yangtze River Economic Belt in recent years.
Second, there are significant regional differences in the green development efficiency of the chemical industry in the Yangtze River Economic Belt. From the perspective of space, the green development efficiency of the chemical industry in the lower reaches is high, while that in the middle and upper reaches is low. In terms of time, the overall regional differences and interregional differences tend to narrow, and the intraregional differences expand. From the source of difference, regional difference is the main source of regional difference in green development efficiency of the chemical industry in the Yangtze River Economic Belt.
Third, there is σ-convergence in the green development efficiency of the chemical industry in the whole region, the middle reaches, and the lower reaches of the Yangtze River Economic Belt, while there is no σ-convergence in the upper reaches. There are β absolute convergence and β conditional convergence in the green development efficiency of the chemical industry in the whole region and the upper, middle, and lower reaches of the Yangtze River Economic Belt.
Fourth, there is spatial heterogeneity in the impact of environmental regulation, industrial structure, foreign capital intensity, and scientific and technological progress on the green development efficiency of the chemical industry in the Yangtze River Economic Belt. This spatial heterogeneity suggests that in the governance of the chemical industry in the Yangtze River Economic Belt, common but differentiated policy measures should be formulated and precisely applied according to the characteristics of the region, by region and by industry. The middle and upper reaches should strengthen environmental regulation, adjust the structure of chemical industry, strengthen supervision and management of foreign investment, and improve environmental access standards. The lower reaches should focus on scientific and technological innovation, promote the upgrading of chemical industry value chain, and enhance the resilience of chemical industry.
It should be noted that, due to the limitation of statistical data, the paper uses the proportion of COD and ammonia nitrogen (NH + 4 -N) emissions in industrial wastewater of the chemical industry by industry nationwide to estimate the COD and ammonia nitrogen (NH + 4 -N) emissions in wastewater of the chemical industry by industry in each province when calculating the gray water footprint, without distinguishing the proportion of COD and ammonia nitrogen (NH + 4 -N) emissions in wastewater between provinces and regions differences. To some extent, this underestimates the differences in gray water footprints of chemical industries in the Yangtze River Economic Belt among provinces and cities. Meanwhile, the study of the differences in the overall green development efficiency of the chemical industry in the Yangtze River Economic Belt and its convergence can help to grasp the green development efficiency of the chemical industry in provinces and cities in general, but in-depth studies on the gray water footprint and green development efficiency of five subsectors are needed in the future to reveal the differences between industries and to develop more refined governance strategies for the chemical industry.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.

Conflicts of Interest:
The authors declare no conflict of interest.


What matters in adoption of small-scale rain water harvesting technologies at household level? Evidence from Charco-dam users in Nzega, Tanzania

Abstract Any effort to improve irrigation water availability has an advantage in the crop production processes. Small-scale rainwater harvesting technologies like Charco-dams (Malambo) in Swahili (the language used by most the Tanzanians) are among the interventions proven to overcome agricultural water shortage. Despite its importance in overcoming water stress in most arid and semi-arid areas of Tanzania, some farmers in the areas are still reluctant to adopt such technology. In this regard, this analyzed and discussed the factors influencing the adoption of the Charco-dam rainwater harvesting technology by smallholder vegetable producers in Nzega district, Tanzania. A survey was used to collect the required information from 528 smallholder vegetable producers in the district. A structured questionnaire was employed to collect data from 220 Charco-dam adopters and 308 non-adopters who were used for the analysis. The data collected was substantiated by six focus group discussions (FGDs); one at council headquarters and five at the village level. The Probit model with instrumental variables was used to identify and analyze factors influencing adoption. The study revealed that socioeconomic, farm level and information sharing factors are all important to improve the adoption of Charco-dam technology. The results suggest that any strategy, innovation or policy aimed at increasing the adoption rate of small-scale rainwater harvesting technologies should be designed or formulated by considering socioeconomic, farm-level, environmental, and information sharing aspects.

Introduction
Agriculture and specifically crop production is the most water consuming sub-sector across the globe, simply because water is an essential and primary factor that influences crop productivity and hence guarantees high yield which in return determines small-holder farmers' wellbeing (Kelemewerk Mekuria et al., 2020;Smith et al., 2011). Apparently, agriculture is inherently sensitive to weather and climate condition, and is among the most vulnerable sectors to the risks and impacts of global climate change and variability (Chartzoulakis & Bertaki, 2015;Kannan & Anandhi, 2020;Mahoo et al., 2015;Rosa et al., 2016;Tzanakakis et al., 2020). It has been agreed that the world is facing a threat from global warming which is causing droughts, flooding, storm damage, long-term shortages of water, worsening soil conditions, desertification, as well as disease and pest outbreaks on crops and livestock (Iizumi & Ramankutty, 2015;Lewis et al., 2018;Rosenzweig et al., 2014Rosenzweig et al., , 2002. Consequently, vulnerable areas such as arid and semi-arid areas are experiencing severe losses in agricultural productivity, primarily due to reductions in crop yields (IPCC, 2007;Kang et al., 2009;Ochieng et al., 2016). Therefore, any efforts to improve irrigation water availability have advantages in the crop production processes. According to Gebre and Rahut (2021), East Africa countries experience much higher prevalence of food insecurity due to change in climate and its variabilities as compared to other places in the world. For so many years, small-holder farmers in Tanzania have been facing agricultural water shortage, that directly affecting farm productivity (Jackson et al., 2018). Small-scale rainwater harvesting technologies like Charco-dams (Malambo) in Swahili (the language used by most of Tanzanians) are among the interventions proved to overcome agricultural (for both crops and animals) water shortage. Such technologies observed to have advantages in terms of simplicity in its architectural design, management and control, hence expected to be highly adopted among the arid and semiarid areas (Awulachew et al., 2005;Mati, 2012;Nissen-Petersen, 2006;Stephens, 2010;Wagner, 2005).

Statement of the problem
Charco-dam has been used by farmers in several arid and semi-arid areas for domestic use, irrigating the crops as well as for watering the livestock. Hence the use of the water from these dams are differentiated from one area to another based on the water-use, management level, size as well as shapes of the facility, this is mainly due to socio-economic status of the people in particular area (Barron et al., 2009;Hatibu et al., 2000). Unlike other regions in Tanzania (i.e. Shinyanga, Dodoma, Arusha, Tabora, Singida and Mwanza regions), where Charco-dams are owned and managed by community or group of people, Charco-dam in Nzega are owned and managed mostly by individuals mainly for crop farming. Due to the challenges brought by the communal managed and controlled water sources (such as public ponds &dams, wet-valleys & catchments), which include limited frequency and amount of water accessed, and to some catchments limitation on the type of crop to cultivate, small holder vegetable farmers in Nzega are expected to highly adopt this technology, so as to enjoy the unique characteristic of this type of dam to overcome water management and control issues, simply because the production decision is purely based on one decision maker (owner of the irrigation facility) and not majority in the community, and hence enables the adopters to increase yields. However, the assumption is not holding true in Nzega District, whereby adoption of such a technology has been very low (30%-40% across the surveyed wards). Therefore this study analyzed and discussed the socioeconomic, farm level and information sharing related factors that influencing the adoption of Charco-dam among small holder vegetable farmers in Nzega district.

Adoption of technology
Adoption can mean different ways depending on the context of its usefulness. It can be choosing, or continued use of recommended idea or practice, not necessarily be new but novel (Baumüller, 2012;Dasgupta, 1989;Hall & Khan, 2002;Rogers, 2003). According to Rogers (1962), adoption is the mental process that starts when an individual first hears or subjected to the information about certain technology and ends to its final adoption or rejection. Rogers (1983) has distinguished the process in five phases: (1) The knowledge phase: the time when an individual becomes aware of a technology after being exposed to it and gains some idea of how it works; (2) the persuasion phase: this is when the individual starts to interpret the technology (favourable or unfavourable); (3) the decision phase: in which person engages in actions that lead to a choice (adopt or reject the technology); (4) the implementation phase: in this stage the individual puts a technology into use; (5) the confirmation phase: this is an evaluation stage where the individual measure the success or failure of the decision made. In all the five stages, flow of information and particularly how individual perceive the technology has been an important factor for adoption outcomes.

Measuring adoption of agricultural technologies
Agricultural dynamism due to change in natural conditions, resource availability as well as other socio-cultural issues made adoption of agricultural technologies unavoidable (Cimmty, 1993). Since the introduction of adoption models in agriculture by work of Griliches (1957), agricultural economists have extensively been using the concept in various studies to analyze adoption of various agricultural technologies and their effects on agricultural performance (Adesina & Baidu-Forson, 1995;Akinola et al., 2010;Kimani et al., 2015;Letaa et al., 2014;Nkegbe et al., 2011). There are many models for measuring processes in which an individual engages in adopting a new innovation (Straub, 2009). Since the adoption is an individual decision-making process, then it is the economic choice decision whereby an individual decides to opt for a certain technology if the chosen technology will provide desired expectations, given socio-economic background of the individual. Knowing that, the stages distinguished by Rogers in 2003 are further manipulated to give the two main paradigms; (1) analysis of the adoption of technology as discrete case, and (2) analysis of adoption as a continuous case. In the first case, dichotomous choice models such as logit and probit models are usually employed to identify and evaluate the determinants of adoption for particular agricultural technologies (Aneani et al., 2012;Ayuya et al., 2012;Kaliba et al., 2000;Kijima et al., n.d.;Letaa et al., 2014). While for the second case, different types of censoring and truncated models such as Tobit and Heckman models are usually employed to explore the extent or effects or impact of the adopted technologies on various aspects in farmers' livelihood and farming performance (Arslan et al., 2013;Baidu-Forson, 1999;Bokusheva et al., 2012;Hailu et al., 2014;Wang et al., 2012).

Study area, research design, sampling and data collection methods
A survey design was used to gather the required information from small-holder vegetable farmers in Nzega District. The district is one of the seven districts of Tabora Region in Central Tanzania, receives annual rainfall between 650 mm and 850 mm, with the annual temperature ranging between 28 to 30 °C, 1 while October and July are the warmest and coolest months, respectively. Such rainfall pattern and temperature extremes make the district one of the hottest and driest districts in the region; which is distinguished as a tropical savanna climate and typically pronounced by a dry season, or mostly referred to as semi-arid.
The survey involved 528 small-holder vegetable farmers, 1 district, 5 wards and 5 villages were purposefully selected. The criteria for selection were based on the presence of CDT and the nature and type of crops cultivated (vegetables for this case). Through respective ward and village extension officers, small-holder vegetable producers in each village were identified, and the lists comprised both adopters and non-adopters of the CDT were developed in each village, formula of Yamane (1967) was used to determine required number of farmers in each village (Table 1). Lastly, random selection was done to obtain the number of respondents selected from each village, to get 220 adopters of Charco-dam and 308 non-adopters. Respondents were interviewed using a structured questionnaire, and a total of six focused group discussions (FGDs) were conducted; one at council headquarters and five at village level, to validate and substantiate the information collected during survey.

Theoretical framework
Adoption of agricultural technology involves a number of characteristics that influence an individual decision option, either to adopt or not to adopt the technology (Adesina & Baidu-Forson, 1995;Misaki et al., 2016;Pierpaoli et al., 2013;Rodríguez-Entrena & Arriaza, 2013;Simtowe et al., 2012;Wu et al., 2010). It is noted that for an individual farmer to adopt a certain agricultural related technology, the utility derived from adopting the technology should be higher than the expected utility of not adopting (Afolami et al., 2015). Regarding that, small-holder vegetable farmers in Nzega district are assumed to be rational producers, in the sense that their production choices are in accordance with their preferences, then the equation to model their adoption process is based on utility maximization theory (Adesina & Zinnah, 1993;Sidibé, 2005;Zepeda, 1994). The model assumes that the decision to adopt a particular farm level production technology is based on the maximization of an underlying utility function and a farmer selects his/her production technologies based on his/her expected utility. Therefore a function used to estimate the adoption of Charco-Dam Technology (CDT) can be expressed as: Where C � i is a latent variable denoting the difference between the utility from adopting Charco-Dam technology (U iA Þ and the utility from not adopting the technology U iNA ð Þ. The farmer will The term X 0 i β provides an estimate of the difference in utility from adopting the technology U iA À U iNA ð Þ; using the household socio-economic, farm-level and information-sharing fators (X i Þ as explanatory variables-which assumed to be exogenous, and μ i is an error term. Basically under this situation, several choice models such as generalized probability models like Linear Probability Model (LPM), Control Function (CF), or Maximum Likelihood (ML) approaches are appropriate to estimate the adoption equation (1; Afolami et al., 2015;Ding et al., 2011;Ojo, 2004).
However, these methods have some drawbacks; for instance, LPM ignores the effect of binary outcome which may easily provide unacceptable predictions resulting to have over or under estimation (Valente et al., 2018); for CF, it is mostly observed to fit continuously distributed endogenous variables and observed to be weak in estimating discrete endogenous variables (Bontemps & Nauges, 2017;Kalisa et al., 2016); while for ML, despite its efficiency in handling discrete endogenous covariates, it requires a complete parametric specification of dependency of each endogenous covariate on error term, failure to have a proper specification may lead to endogeneity problem (Chesher et al., 2013;Greenland, 2000).

Analyzing adoption of Charco-dam technology
In cross-sectional data, endogeneity problem can arise when there is measurement error or omission of one or more important variables in the model (Koladjo et al., 2018;Lu et al., 2018). One of the solution to the problem is to use instrumental variable (IV) that replace the endogenous variable with a predicted value that has only exogenous information (Becker, 2016;Chesher et al., 2013). Following the works of Heckman et al. (2006), Bollen (2012), and Angrist and Krueger (2001), the adoption equation (1) becomes: Where C � i is the binary decision variable (to-adopt or not-to adopt CDT), X 0 i is the vector of explanatory variables (assumed to include at least one endogenous variable), β are the parameters to be estimated and μ i is the error term, that assumed to have zero mean distribution. I(•) is the indicator function taking the value 1 if the latent variable X 0 i þ μ i � 0 is to adopt CDT and zero otherwise. To overcome the possibility of mis-specify the endogenous variables in vector X 0 , an instrument variable Z was introduced in equation (3), which assumed to be uncorrelated with the random shock μ i , as; From equation (3), the structural binary-choice model is as follows: � Therefore, this study analysed the adoption of CDT using instrumental variable (IV) equation (4) whereby Z 0 is the instrumental variable and γ is the parameter to be estimated for instrumental variable, the rest of variables are as in equation (3). For comparison purposes, results from LPM as well as normal probit regressions were also reported (Appendix A). Note, number of labour involved in vegetable production are assumed to be influenced by household-size (for family labour dependants) and the income of household income (for hired labour). Number of labours used in vegetable production assumed to influence adoption of CDT, the number of labours is as well assumed to be influenced by household income (for hired labour) and household-size (for family labour), but the variables household income and household-size are also expected to influence CDT. In such situation, at least one of the explanatory variables can be endogenous, and hence there is a chance of mis-measurement error if otherwise. One of the solutions to overcome this is the use of instrumental variable approach (Lewbel, 2000). Instrumental variables for this case are variables "number of labour used for farm-activities (NoLabourFA)" and "household-income (Hhinc)", are replacing variable household-size (Hhsize), which is assumed to cause endogeneity problem in the discrete choice model. The description of variables used in the adoption model is given in Table 2.

Characteristics of adopters and non-adopters of CDT
The summary statistics of the socio-economic, farm-level and information-sharing factors for adopters and non-adopters of charco-dam technology (CDT) are given in Table 3. It was observed that, 14 out of 22 (64%) of the compared variables were statistically insignificant, this gives an indication that the two groups are likely homogenous and therefore can statistically be compared. However, for the socio-economic variables, it was observed that male household heads occupied a larger share in both overall of the surveyed samples (86.93%), as well as for adopters (90%) and non-adopters (84.74%) categories. The difference of about 5% between the two groups was significant at 10%. No statistical difference observed for the households with formal education between the two groups (adopters and non-adopters of CDT). Mean age for non-adopters of CDT observed to be younger (37.43 years) than that of adopters (39.51%) and the difference is significant at 5% level. Mean household size for CDT-adopters observed to be higher (6.70) than that of non-adopters (3.98), and the difference were significant (at 1%). Further, results show that, the mean annual off-farm incomes for CDT-adopters was 1,361,548/ = TZS) while that of non-adopters was 1,172,404/ = TZS, these results were statistically insignificant. For farm-level variables, no statistical difference observed between adopters and non-adopters for the individuals who owns the vegetable cropping land. It is also observed that, 56.36% of CDT adopters are involved in various micro-credit schemes while only 26.30% of non-adopters of CDT are involved with such schemes, the difference is statistically different at 1% level.  Moreover, it was observed that, only three (30%) out of ten variables indicating farm-level characteristics are statistically different (i.e. land-size, those planted sweet-pepper and labour used in farming), the rest (i.e. use of inputs [seeds, chemicals and fertilizer], those planted other vegetables [tomato, cabbage and leafy vegetables]) are not statistically different. In addition, only 1 variable (Membership to community development groups) of the four information sharing characteristics examined was statistically different (at 1% level of significance). Majority of the development groups formulated in the villages or wards level are based on the agriculture, environment and social activities. The attention were given to the groups that were randomly accepting the membership, with the purpose of tracking their influence in improving members knowledge through interactions. No statistical difference observed for the two groups (adopters and non-adopters of CDT) in the; farm production variables, which include: use of improved seed, use of chemicals and use of inorganic fertilizers, as well as for the all information-sharing variables, which include: access to extension service, access to radio behaviour and access to mobile-phone (Table 3).

The diagnostic statistics of the regression models
Generally, Table 4 shows the results of probit model with instrumental variables (IVProbit). This model was having a Wald chi 2 of 368.62 (significant at 1% level), and Wald test of Exogeneity of 46.63 (Significant at 1% level). The IVProbit model taking the ML assumptions as a normal probit, but assume that the explanatory variables contain exogenous variable and at least one continuous endogenous variable. Using proper instrumental variable-for this case variables (NoLabourFA) and (HHinc), replaced variable (Hhsize)-override the problem of mis-measurement error observed in normal probit model. Both simple check of the Wald test of exogeneity from a "ivprobit regression" by Stock and Yogo (2002) as well as the test of IV strength by Olea and Pflueger (2013) were applied and found that the selected instrument were statistically strong. The fact that ivprobit is robust over LPM and normal probit regression, the results for IVProbit model were considered in the discussion.

Determinants of Charco-dam technology adoption decision
Results in Table 4 show that household size was very significant (at 1%) and positively related with the probability of adopting CDT. The results further indicates that, one more increase in household member increases the likelihood of adopting the CDT by 0.815. Literature claims that, farming households with more family members tend to have more labour and have more chances to adopt agricultural technology than household with less family members (Kansiime et al., 2014;Kebede et al., 1990;Lambrecht et al., 2014). Likewise for the charco-dam technology (CDT), the nature of the CDT which is labour-intensive (especially during contraction of the dam, channeling the water to the dam, irrigating the crops, controlling siltation and enlarging the dam size), usually dictates the adopters to have such manpower at household level.
Most of the agricultural technologies especially those related to soil and water have accompanied by initial cost of investment (Atampugre, 2014;Yigezu et al., 2018). Thus, farmers who have substantial and consistent incomes are more likely to adopt these technologies compared to those who have low and/or inconsistent incomes (Alam, 2015). Likewise, farmer with access to credit enables him/her to have enough money to cutter for any initial or operation costs that associated with the agricultural technology opted (Abdallah, 2016;Mekonnen, 2017;Obayelu et al., 2017;Obisesani et al., 2016;Ullah et al., 2018). The findings in Table 4 are also in-line with this allegation, as individual vegetable producers in the study area who accessed micro-finance (tracked through membership to various microfinance schemes for the past three years), observed to have 0.328 likelihood of adopting CDT than those who were not members of any credit scheme, the effect is statistically significant at 5% level.
Basically, small-holder vegetable producers in the study area have two main sources of water to irrigate their vegetables; the first source is community ponds, which include open ponds, dams, wet-valleys and catchment areas that have been established and managed by the community (in this study referred as other sources of water -OSW); the second source of water is from charcodams (CDT), which is individually established and managed at household level (the major concern of this study). Both adopters and non-adopters of CDT assumed to have an access to OSW upon adhering to the location and existing water use regulations. So, results in Table 4 show that, use of OSW is very significantly (at 1%) and negatively related with the probability of adopting CDT. Given the fact that irrigation investments (both local and improved) are cost effective, in terms ofeconomic, technical and environmental benefits, among others (Nissen-Petersen, 2006;Rwehumbiza, 2007), it was observed that farmers with alternative sources of water for irrigation reduce the chance of adopting CDT by 0.647.
Generally, vegetables are water demanding crops regardless of their agronomic requirements. However, the amount of water required per crop with respect to agronomic specification is varying from one vegetable-crop to another. Farmers with less access to water for irrigation would prefer less water demanding vegetables and the vice-versa holds true. Results in Table 4 show that cultivating tomato, cabbage and leafy-vegetables increase the chance of adopting CDT. Since all three vegetables are water demanding crops, producing such crops observed to increase the chances of adopting the CDT by 0.453 for tomato (at 5% level of significance), 0.518 for cabbage (at 10% level of significance) and 0.402 for leafy-vegetables (at 5% level of significance).
Essentially there is a close positively relationship between access to information and adoption agricultural technologies. Farmers who have access to agricultural information tend to have a wide knowledge and skills on various agricultural related issues, thus increase the diffusion of the various agricultural technologies and eventually increase adoption of the technologies (Baloch & Thapa, 2016;Chandio & Jiang, 2018;Maffioli et al., 2013;Nakano et al., 2018). This has also been observed, as results in Table 4 show that, access to agricultural-related information has a strong likelihood and positively influence the adoption of the CDT, as the variables associated with the acquisition and sharing of information at rural setup, i.e. membership to social and community groups is significant at 5% level.
Individuals interaction to various social and development group in the community has been proved to provide social networks, relationships and linkages that enable individuals to cooperate, coordinate, share information and resources, and eventually act collectively, which all together can increase their likelihood to adopt various agricultural related technologies (Ali et al., 2007;Hansen & Roll, 2016;Hunecke et al., 2017;Husen et al., 2017). Likewise, this study has also evaluated the status of respondents' social capital through membership of vegetable farmers to various social and development groups in the study area and found that, membership to various social and development activities groups in the study area improves the likelihood of adopting CDT by 0.296.
On the other hand, previous studies observed that farmers who are often listening to agricultural radio programs had more chances to adopt various agricultural related interventions than those who are rarely or not listening those programs at all, simply because this behaviour let them grasp more information about agriculture that stimulate their desire to adopt various agricultural technologies (Agwu et al., 2008;Alia et al., 2013;Manda & Wozniak, 2015). The same has been observed in this study, as the results in Table 4 show that frequency of radio listening behaviour can increase the probability of CDT adoption by 0.306, this can be attributed by the fact that, most of the individual small-holder vegetable farmers in the study area who have a desire of improving their wellbeing through improving their vegetable production requires more information regarding vegetable production, this has pushed them to solicit the information to elsewhere possible including radio. The information gathered through focused group discussion reveals that most of the farmers listening to agricultural-related radio programs which focusing on weather forecasting, Rain-Water Harvesting technologies, agricultural productivity (specifically on vegetables) and market information (both input and output markets), most of these individuals have also happened to adopt CDT as well.

Conclusion and recommendation
Generally, the results show that household size, access to credit, choice of crop (especially tomato, cabbage and leafy-vegetables) and access to information have positive influence on adoption of CDT, while use of other sources of water for irrigation (as an alternate-e irrigation-water source) was having a negative relationship with the adoption of CDT. Having these results, it can be concluded that, given the nature of the technology, socio-economic, farm-level and information-sharing factors are all important to enhance the adoption of the technology. Thus it is suggested that, any strategies or innovation or policy geared to improve the adoption rate for such technologies should be designed or developed while considering the three key factors, i.e. socio-economic, farm-level as well as the information sharing aspects. For instance, (i) microfinance institutions should develop special loans to small-holder vegetable farmers to overcome initial investment for such technologies; (ii) Local Government Authorities, Extension Officers and agricultural development partners should use progressive farmers to instill knowledge of such technologies to fellow farmers in their development groups, as well as; (iii) using media campaigns to instill the awareness to farmers on CDT technology and related issues, such media could be local radios and social media groups in ward and village level.

Appendix A General Information of the Used Models
Three models were considered to analyze the determinants of CDT adoption. Generally, the results in Appendix B show that, model (1) and model (2) have related findings in terms of sign, they only differ in terms of the magnitudes. While results for model (3) were different from the rest.
Model (1)-is Linear Probability Model (LPM), taking the assumptions of Ordinary Least Square (OLS), the model found to have R-square of 0.5891, which indicates that the independent variables were able to explain the adoption effect by only 58.9%.
Model (2)-is normal probit regression taking the assumptions of Maximum Likelihood (ML) while assuming no-endogeneity explanatory variables. This model was having a Waldi chi 2 of 185.52 (significant at 1% level), and a Pseudo R 2 of 0.5811, again indicating that only 58% of the variation in dependent variable are explained by independent variables. Since, this model assumed all of the explanatory variables are exogenous, hence there is a chance of mis-measurement error if otherwise (Lewbel, 2000).
Model (3) shows the results of probit model with instrumental variables (IVProbit). This model was having a Wald chi 2 of 368.62 (significant at 1% level), and Wald test of Exogeneity of 46.63 (Significant at 1% level). The IVProbit model taking the ML assumptions as a normal probit, but assume that the explanatory variables contains exogenous variable and at least one continuous endogenous variable. Using proper instrumental variable-for this case variables (NoLabourFA) and (HHinc), replaced variable (Hhsize)-override the problem of mis-measurement error observed in normal probit model.

Variables
Model (