A quantile regression approach to assess the impact of water-related environmental innovations on water stress

Ömer Esen, Durmuş Çağrı Yıldırım and Seda Yıldırım

Technological Forecasting and Social Change, 2024, vol. 203, issue C

Abstract: This paper examines the further question of whether water-related environmental innovations affect water stress in three main sectors such as agriculture, industry and services. The sample consists of 30 OECD countries for the period 2000–2019. This paper employs the method of moments quantile regression (MMQR) with fixed effects to analyze the effects of water-related innovative activities across different quantiles of water stress for all three sectors separately. The empirical results indicate that water-related innovations positively affect water stress in the agricultural sector in the lower and middle quantiles, while it has a negative effect in the middle and higher quantiles for the services sector and the higher quantiles for the industrial sector. These findings reveal nuanced effects of water-related innovations on water stress across sectors. Furthermore, the study suggests that while innovations in the industrial and services sectors can mitigate water stress beyond a certain threshold level, the agricultural sector appears to benefit comparatively less from such innovations. These findings underscore the importance of complementing water-related innovations with appropriate regulatory measures, to address informal practices such as losses and leakages in agricultural irrigation. Ultimately, this study provides valuable insights for policy makers to formulate targeted environmental policies and strategies, especially in the context of climate change adaptation, thereby enabling a more effective allocation of resources to mitigate water stress within each sector.

Keywords: Water stress; Water-related environmental innovation; OECD; MMQR (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:203:y:2024:i:c:s0040162524001392

DOI: 10.1016/j.techfore.2024.123343

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