The Nexus between Agriculture, Water, Energy and Environmental Degradation in Central Asia—Empirical Evidence Using Panel Data Models

: The primary aspiration of this paper is to learn about the effects of economic growth, energy consumption, agriculture and irrigation water consumption and agriculture productivity on environmental pollution in ﬁve countries of Central Asia. The data cover the period from 1992 to 2020 by applying panel data models, namely the Panel FMOLS, Panel DOLS and Panel ARDL-PMG approaches. The results indicate that there is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production on CO 2 emissions while agriculture value added and trade openness have a negative and statistically signiﬁcant inﬂuence on CO 2 emissions in Central Asia. Country speciﬁc short-run coefﬁcients from Panel ARDL reveal that energy consumption is the main driver for rise in the level of CO 2 emissions in the countries under the study. Indeed, country level analysis generates unique nexus correlation among agriculture, energy and environmental degradation in each country of Central Asia.


Introduction
The primary environmental issue facing humanity over the past few decades has been climate change. The main cause of the greenhouse effect, carbon dioxide emissions (CO 2 ), are thought to be the most significant environmental issue facing the planet. These greenhouse effects put human life at risk [1]. In the case of Central Asia (CA), pressure on energy and water resources are growing steadily [2]. However, environmental issues are caused by rising energy demand and greenhouse gas emissions in the region [3]. Given their heavy reliance on fossil fuels for energy, Central Asian nations produce significant amounts of CO 2 emissions because of the available resources [4]. Regarding the usage of renewable energy sources, there is a considerable lack of attention in the region [5].
Gross Domestic Product (GDP), agriculture, water, energy, fossil fuel energy consumption and trade elements are regarded as having high impacts on the countries of Central Asia [6]. Due to rising human demand and the effects of climate change, the value of water as a natural resource has increased in recent years [7]. The main water sources for the Aral Sea are the Amu Darya and the Syr Darya [8]. Prior to the 1960s, 63 km 3 of water per year on average poured from these rivers into the Aral Sea [9]. In 2006, it was observed that the water flow from these two rivers had considerably decreased up to 5 km 3 /year [10]. The drop was accompanied by a sharp rise in the irrigated area which had grown from 30,000 km 2 (1913) to 79,000 km 2 (2000) [11]. The availability of water resources, particularly

CO 2 -GDP Nexus
For the last years, vast amounts of research have been carried which are based on relationship CO 2 emissions and economic growth and the results differs from country to country because of the factors such as geographic situation, composition of GDP and use of natural resources [24][25][26][27]. ARDL Approach was applied [28] to find out positive correlation between economic growth and CO 2 emissions in Tunisia and Morocco. The Economic Growth and Pollution Nexus in Mexico, Colombia and Venezuela (G-3 Countries) are investigated [29], and the findings show that GDP and CO 2 emissions have a short-term Energies 2023, 16, 3206 3 of 20 negative co-integration but a long-term positive co-integration. In the case of Venezuela , the feasibility of stabilizing CO 2 emissions under a rapid increase in GDP was evaluated [30]. The results indicate that GDP has a favorable impact on the environment and exists in an inverted U-shape EKC [30].
Panel FMOLS and panel DOLS calculations are introduced using a panel data set of 17 OECD nations covering the years 1977-2010. Findings show that GDP per capita and GDP per capita squared have both positive and negative effects on CO 2 emissions, which are in line with the panel's EKC hypothesis [31]. Economic growth and non-renewable energy generation have positive effects on CO 2 emissions [32]. A 1% rise in GDP and non-renewable energy use results in an increase in CO 2 emissions of 0.53 to 0.82% and 0.30 to 0.37 percent, respectively [33]. These calculated coefficients are greater than the study of Wang et al. [34] and less than the findings of Hanif [35].
Similar to this, the authors of [36] study the impact of agriculture on CO 2 emissions in Saudi Arabia and find that once it accounts for more than 3.22% of GDP, agriculture starts to cut CO 2 emissions. A panel cointegration test was used [37] to investigate the correlation with CO 2 emissions in the case of CIS countries.

CO 2 -Agriculture Nexus
By classifying China's 30 provinces into six quantile grades, the authors of [38] examined the variables affecting CO 2 emissions in the country's agricultural sectors. They then used the quantile regression method to look into the factors influencing CO 2 emissions at high, medium and low emission levels. The findings [38] demonstrate that economic growth has heterogeneous effects on CO 2 emissions, which should be taken into account when reducing CO 2 emissions in the agriculture sector. The authors of [39] used the FMOLS, DOLS and PMG techniques to analyze the effects of climate change on agriculture in India, from 1981 to 2017. The authors of [40] built a bottom-up inventory-based model and its findings confirm that agricultural non-CO 2 GHG emissions increased by 34% between 1980 and 2018, and by 2060, they are expected to have increased by another 33%. The authors of [41] concluded differently after utilizing the ARDL cointegration technique to examine the connection between income per capita and agriculture-related CO 2 emissions. Increased CO 2 emissions were highly cointegrated with agricultural contribution. This is consistent with [42] panel data analysis of BRICS utilizing a sample from 1992 to 2013.
In the study [43], data for ARDL from 1980 to 2018 were utilized to examine the longand short-term effects of these factors on CO 2 emission in the case of Pakistan. According to the findings, food crop production has the potential to reduce CO 2 emissions in the short-term, but agriculture value added has a long-term, negative relationship with CO 2 emissions [44]. The authors of [45] investigate the interdisciplinary relationship between remittances and CO 2 emission in five countries. The authors of [46] show that for 14 African nations between 1990 and 2013, an expansion in the agriculture value added reduces CO 2 emissions.

CO 2 -Water Nexus
The effects of population growth, freshwater resource stress and increased environmental protection regulations will all have an impact on agricultural production [47]. To improve decision-making in the future, drought management at farm level was examined [48]. To increase the water availability for irrigation when the crop is needed, new methods should be adopted [15]. Several studies [49][50][51][52] have shown that the agriculture industry is one of the most vulnerable to climatic shifts in emerging nations.
The world's food demand is expected to nearly double by 2050, raising serious concerns about the sustainability of agriculture [18]. Climate adaptations refer to a balance between ecological, society and the economy that can be accomplished by altering behavior, consumption patterns and infrastructure [53]. Although sustainable development is regarded as a lengthy, labor-intensive and effective process, it would be advantageous to Energies 2023, 16,3206 4 of 20 adopt it as soon as possible [16,54]. Planning their adaptation is even more crucial [55] than creating integrated climate-land-water strategies [52,56].
Because water is scarce in many parts of the world, the irrigation industry is particularly in need of good water management, especially in Central Asia. This will increase crop output in the agricultural sector [57]. Genetic engineering can be used to create droughtresistant crop varieties, which can help to decrease the amount of water required without lowering output [48,58,59]. Due to rising water demand, there are water scarcity issues in many regions [58].
Semiarid and arid regions, mostly in Central Asia, are particularly susceptible to the effects of climate change and unpredictability on water availability and distribution [60]. The complicated interaction between climate variability and water availability has been quantified using resilience, reliability and vulnerability [61]. When water availability is insufficient compared to demand, there are societal, economic and environmental consequences. This is referred to as a socioeconomic drought [58,62]. In fact, Uzbekistan has the biggest proportion of irrigated land in the entire area vulnerable to drought risk [63]. In the SSP3 long-term scenario, 63.1 percent of the country's irrigated agriculture-which makes up 68% of the overall cropland in the region-is vulnerable to drought [10].

CO 2 -Energy Consumption and Fossil-Fuel Based Energy Consumption
Central Asian (CA) economies as part of ex-Soviet Union countries are well known for their high technical and non-technical losses in the process of energy generation, transmission and consumption [64]. Power transmissions and electricity cables are carrying out high technical pressure leading to the technical loss of around 20% [5]. The entire energy chain was built in the 1960s connecting all Central Asia [65,66]. The energy blackout happened in January 2023 is evidence for the fact that that the whole energy system and its infrastructure are in an emergency situation [67]. One of the reasons behind this energy failure is high concentration on fossil-fuel based energy generation, especially in Kazakhstan, Turkmenistan and Uzbekistan, that leads to increased CO 2 emissions [65,68]. To diversify and decarbonize the energy portfolio of CA, renewable energy generation is the most feasible approach to decrease the pressure on fossil-fuel dependency for energy generation [37].
Using data from four ASEAN nations (Indonesia, Malaysia, the Philippines and Thailand), the authors of [33] investigate the effects of per capita renewable energy consumption and agricultural value addition on CO 2 emissions throughout the period of 1970 to 2013. They demonstrate using the environmental Kuznets Curve (EKC) hypothesis that growing agricultural and renewable energy reduces CO 2 emissions while non-renewable energy has a positive correlation with emissions. The dynamic correlations between renewable energy consumption, agricultural value added, CO 2 emissions and GDP for Brazil were examined [69]. The findings indicate that the variables under consideration have long-run cointegration. A unidirectional connection exists between agriculture, CO 2 emissions and GDP, according to recent empirical studies [70].
Empirical findings from Colombia [71], Indonesia, Malaysia and Thailand [72], China [73] and Australia [74] diversified the aforementioned findings. In general, the variety of results spanning different regions and indicators demonstrates a lack of agreement found in the literature.

CO 2 -Trade Openness
The impact of trade openness on CO 2 emissions has been a subject of considerable research in recent years [75]. Studies have shown that trade openness can have both positive and negative impacts on CO 2 emissions [76]. On the one hand, trade openness can increase economic growth and lead to higher energy consumption and CO 2 emissions [77]. On the other hand, trade openness can promote the transfer of clean technology and improve the energy efficiency of economies, leading to lower CO 2 emissions [78]. The relationship between trade openness and CO 2 emissions was examined using a panel data approach [42]. According to the study, trade openness has a positive effect on CO 2 emissions in China, although this effect is constrained by elements like energy intensity and technical advancement.
Several empirical studies [79,80] have investigated the relationship between trade openness and CO 2 emissions using panel data and time series methods. One of the most notable studies [21] found that trade openness has a positive and statistically significant impact on CO 2 emissions in developing countries. This study [21] suggests that trade openness can increase the use of fossil fuels and lead to higher CO 2 emissions. Trade openness can promote the transfer of clean technology, which can reduce CO 2 emissions. However, in countries with low levels of technological development, trade openness may increase energy consumption and CO 2 emissions [81]. As a result of technological advancements enabling cleaner production, the economies experiencing technological transformation observe an improvement in the quality of the environment [82]. Table 1 displays the results of a large-scale sample taken from World Bank data for five developing economies in Central Asia, namely, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, between 1992 and 2020. Data on CO 2 emissions (tons per capita), GDP growth per capita (constant 2015 US$), agriculture (share of GDP), level of water stress (freshwater withdrawal as a proportion of available freshwater resources), energy use (kg of oil equivalent per capita), electricity production from oil, gas and coal sources (% of total) and trade openness (% of GDP) are sourced [83].  Table 2 provides a description of the statistically critical variables that have been included into our model. Due to data limitations, we can only examine a maximum of 144 observations over 7 variables, with only one of those variables dependent on any of the others. The correlation between the various study factors is outlined in Table 3, which can be seen here. The variables of income per capita (lnGDP), level of water stress (lnWATER), energy consumption (lnENG) and electricity from oil, gas and coal (lnELC) are positively correlated with carbon dioxide emissions (lnCO 2 ). Moreover, agriculture (lnAGR) and trade openness (lnTO) are negatively associated with carbon dioxide emissions (LogCO 2 ).

Specification of the Econometric Model
To represent the relationship between economic development, agriculture, water, energy usage, electricity from oil and gas, trade openness and CO 2 emissions, a basic panel model is developed after a thorough review of the literature. This method assesses how different variables contribute to the total amount of CO 2 emissions in the targeted areas: where t is the period 1992 to 2020; CO2 represents carbon dioxide emission per capita; GDP is the per capita economic growth; AGR represents agriculture, forestry and fishing, value added (% of GDP); WATER represents level of water stress; ENG represents energy use; ELC is the electricity production from oil, gas, and coal; TO denotes trade openness.
Using natural logarithms on both sides of Equation (2), we get a log-linear version of the production function, which helps to mitigate heteroskedasticity problems.

Panel Unit-Root Tests
In the initial stage of the econometric analysis, to verify that the data set has stationarity characteristics, different panel unit root tests are used. As each panel unit root test has various statistical drawbacks regarding size and power features, this research employs three kinds of unit root tests to establish the order of integration of series: [84,85] and the Fisher-PP and Fisher-ADF tests [86,87]. To determine whether the first hypothesis, which presupposed the presence of a unit root in the panel data in the time series, is correct, the aforementioned tests have been carried out. Accordingly, the equitation IPS panel unit root tests, when applying the overall method, can be given as: In Equation (4), y it represents the series for country i in the panel over period t; ∆ is the first difference operator; P = 0 for all i, which is the null hypothesis; and P = 0 for at least one i, the alternative hypothesis which is non-existent for a unit root.

Specification of Panel ARDL (PMG) Model
The pooled mean group (PMG) model that was firstly developed [85] to calculate the short-run and long-run estimates of the effect of the employed explanatory variables on the dependent variable. The PMG makes it possible for the short-term parameters to discriminate between the groups, but it assumes that the long-term coefficients are the same for each group. To make matters even more intriguing, the PMG can be used in situations in which the employed variables are either I (1) or I (0). In the end, the Hausman poolability test is employed to determine whether the practice of pooling long-term coefficients is efficient and acceptable. Taking into consideration an ARDL(p,q) model, in which the lag order of the response variable is indicated by the variable p, and the lag order of the independent variables is indicated by the variable q, the expression of the model may be written as follows: where i represents countries (1, 2, 3 . . . 20); t is the year (1992-2020); j is the optimum time lag; and µ it is a fixed effect.
The short-run relationship with error correction models is written as follows:

FMOLS and DOLS Long-Run Estimators
The Fully Modified Least Square, abbreviated as FMOLS, is a technique to carry out the most effective method of cointegrating regression analysis [88]. However, the heterogeneous FMOLS estimator has been utilized for the panel cointegration regression in this investigation [89] since it has the benefit of removing endogeneity bias as well as serial correlation. FMOLS is the optimal method for the panel that has heterogeneous cointegration [90]. Taking into consideration the fact that a panel FMOLS estimation for the coefficient β of model 2 is where, and M i is the lower triangulation of Ω i . The Dynamic OLS estimator has the same asymptotic distribution as that of the panel FMOLS estimation derived [91]. Both the DOLS and FMOLS estimations are performed as shown to confirm the consistency of the outcome.

Juodis, Karavias and Sarafidis (2021) Granger Non-Causality Test
We also perform Granger non-causality tests [92] to examine the sign and the type of temporal relation between CO 2 emissions, economic growth, agriculture, water, energy and trade openness.
We consider the following specification: for i = 1, N and t = 1, T. Without loss of generality and for ease of exposition, x i,t is assumed to be a scalar. The parameters ϕ 0,i denote the individual-specific effects; ε i,t are the errors; ϕ p,i denote the heterogeneous autoregressive coefficients; p = 1, P and β q,i are the heterogeneous feedback coefficients or Granger causality parameters. The null hypothesis that x i,t does not Granger-cause lnCO 2i,t can be formulated as a set of linear restrictions on the parameters in Equation (9): Failure to reject the null hypothesis can be interpreted as x i,t does not Granger-cause lnCO 2i,t . The same applies when x i,t is a k × 1 vector of regressors.

Empirical Results
Firstly, unit root and cointegration tests have been conducted extensively before examining the relationship between the variables in the area of our interest by employing panel ARDL. Table 4 summarizes the findings of stationarity tests performed by utilizing variety of techniques, namely, the augmented dickey fuller (ADF), Phillips Perron (PP) and IPS tests. The findings provide evidence that the variables in our paper are stationary either at their level or first level. Therefore, it can be concluded that the panel ARDL model can be utilized in our analysis. To verify the long-term relationship between variables, the Pedroni panel cointegration technique is used. The cointegration findings that are provided in Table 5 provide evidence that there is a long-term link between the variables, since the p-values reject the null hypothesis that there is no long-term relationship between the variables.

Long-Run Results
We have used PFMOLS and PDOLS analyzes to get the long-term coefficients of the variables after validation of the long-term correlation between the variables. The outcomes of PFMOLS and PDOLS, which are displayed in Table 6, indicate that a 1% growth in the economies of the economies that are the focus of the research generates a 0.604% and 0.726% rise in the amount of CO 2 emissions, correspondingly. The results are in line with the findings [93][94][95][96][97][98]. Whilst, based on the results of panel DOLS and FMOLS, it can be concluded that agricultural value-added has a negative effect on CO 2 emissions over the long term. An increase of each percentage point in the value created by agriculture potentially leads to a decline in CO 2 emissions of 0.762% and 0.419%, correspondingly. This demonstrates that the influence of value-added agriculture on lowering CO 2 emissions may progressively be recognized over the course of time, as shown by the fact that. Agriculture has the potential to both store carbon and lower its overall carbon footprint if it adopts more modern management and technological practices [16,18,56]. The findings of our paper are supported by scholars [33,[99][100][101]. The next variable, water productivity, has a positive and statistically significant relationship with CO 2 emissions, at a 1% significance level. In theory, effective water resource management and productivity can result in decreased emissions [102]. The energy consumption and electricity generated from oil, gas and coal were shown to have a positive and significant impact on CO 2 emissions by the coefficient values of 0.500 and 0.558 of the DOLS model. The estimated outcomes showed that a 1% increase in energy consumption causes an increase in CO 2 emissions in Central Asian countries. The authors of [103][104][105][106] claimed that the increase in CO 2 emissions is attributable to a 1% rise in the amount of energy used. The findings of panel ARDL, which are given in Table 7, have also supported the results of panel DOLS and FMOLS, accordingly. Trade openness has a negative sign and is statistically significant at the 1% level. This finding suggests that liberalized trade is associated with better environmental conditions (i.e., decreases CO 2 emissions). As shown by the coefficient value by panel DOLS, FMOLS and ARDL approaches, a 1% increase in trade openness results in a 0.672%, 0.706% and 0.224% reduction in CO 2 emissions, respectively. Because of the increased rivalry brought about by trade liberalization, domestic manufacturers are now more likely to adopt cuttingedge equipment in an effort to reduce manufacturing costs per unit of output, which in turn reduces carbon waste and emissions [107][108][109]. In the short run, we analyze the country specific relationship between CO 2 emissions and independent variables. There is a positive correlation between economic growth and CO 2 emissions in Kyrgyz Republic, Tajikistan and Turkmenistan, whilst agriculture has a negative and statistically significant connection with CO 2 emissions in only Kazakhstan. In Turkmenistan, water productivity has a positive and significant connection with CO 2 emissions. Interestingly, energy consumption is positive and statistically significant at 1% level in Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, while electricity from oil, gas and coal is positive and statistically significant at the 5% level in Kazakhstan and Turkmenistan.
We also performed the Granger non-causality test [92] to examine the causality direction. Table 8 summarizes the findings of this investigation. According to the findings of the Granger non-causality test that are shown in Table 8, quite several associations have been found to be as follows: we find at the 1% level of significance that there is bidirectional causation between lnCO 2 and other variables (lnGDP, lnAGR, lnWATER), and unidirectional causality from lnENG and lnELC to lnCO 2 . In addition, there is no evidence that indicates a nexus between the liberalization of trade and the deterioration of the environment. To get more precise and accurate information regarding the findings of the Panel FMOLS, DOLS and ARDL techniques, we construct a graphical illustration (Figure 1). In this graph, blue lines indicate positive and statistically significant at 1% level, whilst red lines are negative ones.
To get more precise and accurate information regarding the findings of the Panel FMOLS, DOLS and ARDL techniques, we construct a graphical illustration (Figure 1). In this graph, blue lines indicate positive and statistically significant at 1% level, whilst red lines are negative ones.  Figure 2 shows the graphical representation of the findings of Granger noncausality test which can be seen in Table 8. According to the graph, agriculture, water and GDP have bi-directional causality on CO2 emissions, whilst energy has unidirectional relationship with CO2 emissions. Interestingly, electricity production from oil, gas and coal and trade openness have no causal relationship with CO2 emissions in the countries under the study when all countries are treated as a group.  Figure 2 shows the graphical representation of the findings of Granger non-causality test which can be seen in Table 8. According to the graph, agriculture, water and GDP have bi-directional causality on CO 2 emissions, whilst energy has unidirectional relationship with CO 2 emissions. Interestingly, electricity production from oil, gas and coal and trade openness have no causal relationship with CO 2 emissions in the countries under the study when all countries are treated as a group.
To get more precise and accurate information regarding the findings of the Pane FMOLS, DOLS and ARDL techniques, we construct a graphical illustration (Figure 1). In this graph, blue lines indicate positive and statistically significant at 1% level, whilst red lines are negative ones.  Figure 2 shows the graphical representation of the findings of Granger non causality test which can be seen in Table 8. According to the graph, agriculture, wate and GDP have bi-directional causality on CO2 emissions, whilst energy ha unidirectional relationship with CO2 emissions. Interestingly, electricity production from oil, gas and coal and trade openness have no causal relationship with CO2 emissions in the countries under the study when all countries are treated as a group. Furthermore, we employ Granger non-causality test to further check if there is a strong causality between CO 2 emissions and dependent variables in this study. When we rank the CO 2 emitters by country (Table 9), it can be shown that the main emitters of CO 2 are energy and electricity from oil, gas and coal. In Kyrgyz Republic, CO 2 emissions are mainly caused by agriculture, energy, water and electricity from oil, gas and coal. In the case of Tajikistan, GDP and electricity from oil, gas and coal are the ones which generate CO 2 emissions. GDP-CO 2 24.924641 *** 0.2442349 *** 0.0000 Agriculture → CO 2 35.594333 *** −0.6423979 *** 0.0000 Water → CO 2 10.534225 *** 0.4054786 *** 0.0012 Energy → CO 2 18.018716 *** 1.734604 *** 0.0000 Electricity from oil, gas, and coal → CO 2 6.47384 *** 2.339287 *** 0.0109 Trade → CO 2 3.3811766 ** −0.1676981 ** 0.0659 Standard errors in parentheses: *** p < 0.01, ** p < 0.05.

Discussion
The Aral Sea's compounding risk scenarios must be taken into account in shaping solutions for climate change adaptation. It is evident that upstream countries (Tajikistan and Kyrgyzstan) mostly focus on water for energy generation while downstream countries discharge water from neighbors for irrigation purposes. At the national level, adaptation and resilience measures must incorporate climate change scenarios in short-and long-term plans [13]. Research on climate resilient development in the Aral Sea region is in its nascent stage which should be focused on the mitigation and resilience approaches to deal with the negative consequences of human made environmental catastrophe in the Aral Sea region.
The Sustainable Development Goals (SDGs) must be attained, and five important adaptation methods for the transboundary risk in CA are suggested: (1) improved dryland agriculture crop production, (2) more robust new infrastructure, (3) improved water resource management, (4) green infrastructure, nature-based solutions and (5) strengthened multi-hazard risk assessment and early warning systems [5,19].
In order to deal with climate fluctuations in agricultural fields, climate smart agriculture (CSA) offers adaptable, society-oriented approaches and solutions. This holistic approach also ensures food security [16]. For instance, Hungary has used a variety of climatic adaptation strategies, including water supply management, particularly in times of scarcity [61]. Because of CSA success stories in industrialized nations, policymakers in Central Asia should create comparable plans to advance CSA in the region.

Policy Recommendations
This research has contributed to the existing literature by examining the connectivity of energy, agriculture, water, economic development and CO 2 emissions in Central Asian countries to provide more insight to policymakers. First, this article discovers that energy consumption and economic growth have a substantial and statistically significant effect on CO 2 emissions. Consumption, urbanization and industrialization have been major contributors to the region's overall economic development during the last several decades. Non-renewable energy sources have been relied upon heavily to provide the energy necessary to meet this rising demand. As a result, increases in both economic development and CO 2 emissions have occurred simultaneously throughout Central Asia. Central Asia's energy strategy should prioritize diversifying the region's energy supply and boosting the use of renewable energy.
Second, the findings suggest that agriculture has a detrimental and statistically significant impact on the level of carbon dioxide released into the atmosphere. Thus, governments in Central Asia must give top priority to the growth of their agricultural sectors and guarantee that this development considers environmental quality to the greatest extent possible. The rise in agricultural value-added, which is regarded to be the growth of the agriculture sector, is inextricably related to the advances in technical capabilities. As a result, the production of value-added goods in the agricultural industry is intrinsically linked to the use of technology, often known as digital agriculture. Using digital technology in agriculture, these countries have the potential to improve not just the value of their agricultural products but also the quality of their local environment. Thus, technological assistance for agricultural producers, as well as expenditures in research and development (R&D) by governments and financial incentives for such work, is a crucial instrument. In addition, it is advantageous for the agricultural producer to make use of technology and to be periodically guided towards the appropriate production and management approaches.
Finally, the results show an adverse relationship between trade openness and CO 2 emissions in Central Asia. To a large extent, the inverse link between economic openness and carbon emissions may be rationalized by pointing to the impacts of technological advancement. Governments in Central Asia should foster preferential trade policies, with a special focus on technological value addition. This may be nurtured via reciprocal trade liberalization and the elimination of trade restrictions. Furthermore, trade openness, in the long run, encourages a virtuous cycle that is good for the economy. This cycle helps the economy by creating job possibilities, easing capital flows and encouraging a competitive atmosphere.
Based on the aforementioned literature review, data analysis and metric calculations, the following future scenarios can be recommended to stakeholders and policy makers: Climate migration is a potential threat resulting from the unsustainability of available resources (water, energy) and industries (agriculture, economy, environment) in Central Asia. The agricultural productivity of cultivated land has decreased because of salt and dust being transported when the northeastern winds blow. As a result of increased health hazards, inadequate nutrition and unemployment, the Aral Sea's degraded water resources are also having a negative socioeconomic impact on the local population. Salinity-related losses are thought to reach more than USD 2 billion annually, or 5% of Central Asia's GDP [2]. Rural populations with fragile ecological and socio-economic conditions will start moving to places offering sustainable economic and ecological conditions. 2.
Smart and water management strategies should be urgently introduced for CA countries. With almost 2000 m 3 per person in 2025, Central Asia will have the highest water withdrawal rates globally [17]. More than 65 percent of the water used in Central Asian nations is used by the agriculture sector where Uzbekistan is a dominant consumer (56 km 3 ) followed by Turkmenistan (28 km 3 ) [2]. Water quality and quantity are expected to continue to decline over the next ten years, so Uzbekistan needs to come up with a water management plan that uses as little water as possible for cotton cultivation [3]. To prevent an excessive amount of water from being wasted, for instance, irrigation management should be improved, and irrigation limitations should be established. It is important to encourage the development of crop types that can resist drought and save water. By implementing above mentioned policies, countries located in Central Asia can tackle the Aral Sea dilemma which is seen as an upcoming disaster for the surrounding locals.

Conclusions
The main aim of this paper is to examine the dynamic relationship between economic growth, agriculture, water, energy consumption, trade openness and environmental degradation in Central Asian countries, namely, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, from 1992 to 2020 by employing dynamic panel data models, including Panel DOLS, FMOLS, Panel ARDL (PMG approach) and Granger non causality tests to see the short and long run nexus between the variables under the investigation. After confirming the level of stationarity and long run cointegration of variables, we proceed to analyze the variables. The findings of Panel DOLS and FMOLS reveal that there is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production from oil, gas and coal on CO 2 emissions in Central Asia at the 1% significance level (Table 6). Specifically, a 1 percent rise in economic growth, agriculture, water productivity, energy consumption and electricity production from oil, gas and coal led to 0.652, 0.435, 0.500 and 0.558 percent increases, correspondingly. These results, additionally, are confirmed by the results of the Panel ARDL, PMG approach. Agriculture value added and trade openness have a negative and statistically significant influence on CO 2 emissions at the 1% significance level. A 1% rise in agriculture and trade openness is responsible for 0.762 and 0.672% decline in CO 2 emissions, respectively. Country-specific short-run coefficients from Panel ARDL reveal that energy consumption is the main driver for rise in the level of CO 2 emissions in the countries under the study. Furthermore, Granger non-causality test is conducted to see the relationship between variables. According to the results of non-causality test, there is a bidirectional connection between CO 2 and economic growth, between CO 2 and agriculture and between CO 2 and water productivity, whilst energy consumption and electricity production from oil, gas and coal have unidirectional causality with CO 2 emissions. Scientific novelty of the paper is that this is the first interdisciplinary and empirical paper integrating six variables in the case of five CA countries. The findings can be used to strengthen regional integration in CA, to forecast future regional development scenarios and strategies, to improve transboundary resource sharing mechanisms and to minimize potential conflicts and threats resulting from water deficiency, energy default, food safety, climate change and environmental degradation. Indeed, technological developments and adaptations of climate-resilient practices can give us better results to deal with climate change. The most important thing is that all stakeholders involved must come together to develop a comprehensive strategy to combat climate change and its detrimental repercussions.
Besides, this paper has several shortcomings. Researchers might analyze the current situation of environmental degradation in Central Asia by including several indicators which affect the level of CO 2 emissions, namely, FDI, urbanization, industrialization and democratization in the future. Additionally, cutting edge time series methodologies can be applied to country-level data by expanding the time span. Data Availability Statement: Research data was obtained from the website of "World Bank Open Data". Estimations were conducted based on this primary data.