Vulnerability of Indian mustard (<Emphasis Type="Italic">Brassica juncea</Emphasis> (L.) Czernj. Cosson) to climate variability and future adaptation strategies

A simulation study has been carried out using the InfoCrop mustard model to assess the impact of climate change and adaptation gains and to delineate the vulnerable regions for mustard (Brassica juncea (L.) Czernj. Cosson) production in India. On an all India basis, climate change is projected to reduce mustard grain yield by ～2 % in 2020 (2010–2039), ～7.9 % in 2050 (2040–2069) and ～15 % in 2080 (2070–2099) climate scenarios of MIROC3.2.HI (a global climate model) and Providing Regional Climates for Impact Studies (PRECIS, a regional climate model) models, if no adaptation is followed. However, spatiotemporal variations exist for the magnitude of impacts. Yield is projected to reduce in regions with current mean seasonal temperature regimes above 25/10 °C during crop growth. Adapting to climate change through a combination of improved input efficiency, additional fertilizers and adjusting the sowing time of current varieties can increase yield by ～17 %. With improved varieties, yield can be enhanced by ～25 % in 2020 climate scenario. But, projected benefits may reduce thereafter. Development of short-duration varieties and improved crop husbandry becomes essential for sustaining mustard yield in future climates. As climatically suitable period for mustard cultivation may reduce in future, short-duration (<130 days) cultivars with 63 % pod filling period will become more adaptable. There is a need to look beyond the suggested adaptation strategy to minimize the yield reduction in net vulnerable regions.     

Can Desalination and Clean Energy Combined Help to Alleviate Global Water Scarcity?

The major present hindrance in using desalination to help alleviate global water scarcity is the cost of this technology, which, in turn is due to energy cost involved. This study examines historical trends in desalination and breaks up the cost of desalination into energy based and nonenergy based. It then develops the learning curves (relationship between cumulative production and market price) for desalination. Assuming that the photovoltaic (PV) technology will be the dominant form of energy used in the desalination process, the existing PV learning curve and desalination learning curve are combined to explore the viability of large‐scale adoption of desalination in the future. The world has been divided into seven regions and it is assumed that water demand from desalinated water will be met only within the 100‐km coastal belt. It is shown that, in most of the regions, other than sub‐Saharan Africa, Central America, and South Asia (where water tariffs are low), the desalination (without considering energy) becomes viable by 2040. For PV technology, less than 1 million MW per annum growth is required till 2050 to make it affordable. Globally, desalination with renewable energy can become a viable option to replace domestic and industrial water demand in the 100‐km coastal belt by 2050.     

Does green environmental innovation really matter for carbon-free economy? Nexus among green technological innovation,green international trade,and green power generation

Environmental Science and Pollution Research - This study examines the association among the green energy production (GEP), green technological innovation (GTI), and green international trade (GIT)...     

The relationship between staple food crops consumption and its impact on total factor productivity: does green economy matter?

The agriculture sector is a key driver of economic growth and provides employment opportunities across the globe generally. However, in today’s world, agricultural product demand is more influenced by taste, prices, and nutritional value due to climatic variation. The study has analyzed the current situation grain productivity by using the data of farm inputs and major grain crops of Pakistan from (1960–2020). The study consists of a two-stage analysis in the first stage, the total factor productivity (TFP) variable is obtained by using the parametric Tornqvisit-Theil index output-input-aggregation method separately for each crop; rice, maize, and wheat. After that, the unit root test is used to check the stationarity and trend of the variables in the long run. Subsequently, the autoregressive distributed lag (ARDL) model is applied to check the existence of cointegration in the long run and short run among the variables. The results of the study disclosed that the consumption of rice has a positive relationship with its total factor productivity, but, wheat and maize have a negative long-run cointegration relationship with the respective productivities. The study results have shown that the consumption pattern of staple crops has substantially changed, due to climatic variation, and the current food consumption trend is revealing new dimensions and trends owing to variation in climate change and anthropogenic pressure which demands to adapt climate resilient farm practices.