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Asia’s water scarcity challenge

                 Table 2. Panel two‑step generalized method of moment estimates
                 Variables                                         Coefficient      S.E.       t‑statistic    Prob.
                 WS(−1)                                              0.878         0.0008       991.661       0.000
                 Governance indicators (used as explanatory variables)
                  GEF                                               −17.581        0.180        −97.388       0.000
                  RQ                                                −55.049        0.162       −339.306       0.000
                 Macroeconomic variables (used as controlled variables)
                  REC                                                1.551         0.098        15.721        0.000
                  POPG                                               −5.172        0.059        −87.506       0.000
                  CROP                                               −0.171        0.003        −55.549       0.000
                  AGLD                                               0.0006       3.3×10 -05    18.527        0.000
                  CLF                                                10.397        0.152        68.164        0.000
                 Effect specification
                  Cross-section fixed (first differences)
                   Mean dependent variable                           1.869          Std. Dev. dependent      19.386
                                                                                         variable
                   S.E. of regression                                28.265         Sum squared residual    708,669.5
                   J-statistic                                       27.795           Instrument rank          40
                   Probability (J-statistic)                         0.679
                 Source: Author’s estimate.
                 Abbreviations: AGLD: Agricultural land degradation; CLF: Climate financing; CROP: Crop production; GEF: Government effectiveness;
                 POPG: Population growth; Prob.: Probability; REC: Renewable energy consumption; RQ: Regulatory quality; Std. Dev.: Standard
                 deviation; S.E.: Standard error; WS: Water scarcity.

                water resources. This research also finds that renewable   agricultural  WS  may  worsen  in  regions  experiencing
                energy sources have a notable beneficial effect on water   soil desiccation and reduced precipitation due to global
                shortage  management.  A  1%  increase  in  REC  leads   warming, ultimately affecting crop yields. Conversely,
                to a 1.551% reduction in WS issues, highlighting the   the  coefficient  for  AGLD  significantly  influences
                potential  of  renewable  energy  to  support  sustainable   water shortage governance. For every 1% increase in
                water resource management. Consistent with previous   AGLD,  water  shortage  problems  rise  by  0.0006%.
                studies,  these  results  are  encouraging:  Areas  facing   This  underscores  the  need  for  more  effective  water
                water shortage can benefit from integrating renewable   management in agriculture. Degraded soil retains less
                energy  sources.  One  major  advantage  of  this  shift  is   water and results in higher runoff and lower groundwater
                reduced reliance on water-intensive energy production   recharge,  reducing  the  availability  of  farmable  water
                techniques such as coal-fired power plants and nuclear   and threatening farmers’ livelihoods. As AGLD directly
                reactors. Transitioning to renewable energy can reduce   contributes  to  water  shortages,  governments  should
                a country’s water footprint in the energy sector, freeing   implement  strategies  to  improve  water  retention  in
                up water for other essential uses.                  soils and prevent further land degradation. Sustainable
                  The  coefficient  for  POPG  indicates  a  significant   land management strategies, such as soil conservation,
                negative impact on WS governance. Specifically, a 1%   afforestation,  and  reforestation,  are  essential  in  this
                increase in POPG corresponds to a 5.172% reduction in   regard.  The  coefficient  for  CLF  shows  a  significant
                WS issues. This finding suggests that, in some contexts,   positive impact on WS governance. A 1% increase in
                POPG may drive improvements in water infrastructure   CLF leads to a 10.397% rise in WS issues. This result
                or governance, although further investigation is needed   highlights the influence of climate change – particularly
                to clarify this dynamic. The coefficient of CROP also   the growing risks of droughts and weather variability –
                shows a significant negative impact on WS governance.   on water availability. Table 3 presents the results of the
                A 1% increase in CROP is associated with a 0.171%   Arellano–Bond serial correlation test for the first and
                decline  in WS  issues.  However,  the  study  warns  that   second lagged terms.



                Volume 22 Issue 2 (2025)                       145                           doi: 10.36922/AJWEP025090057
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