Impact of increasing temperature anomalies and carbon dioxide emissions on wheat production

Climate Change and Wheat Production in Pakistan: An Autoregressive Distributed Lag Approach

Purpose This study analyses the impact of climate change on price stability in South Africa by using quantitative analysis of annual data from 1990 to 2020. Design/methodology/approach The study runs unit root tests to test whether a time series variable is stationary. The auto-regressive distributive lag (ARDL) approach for cointegration is employed to evaluate the long-run impact of climate change on price stability. Findings The ARDL bound test analysis illustrates a cointegration relationship between the CPI and the three macroeconomic (interest rate, oil price and real effective exchange rate) and two climate change indicators (rainfall and temperature) variables. Results show that both temperature and rainfall have a significant negative relationship with inflation, hence climate change should be part of a macroeconomic policy framework. While some studies found a negative relationship between inflation and temperature in seasons other than summer, the current study found a negative relationship probably because it used annual data. Interest rate and oil price show a significant positive long-run relationship with inflation, while real effective exchange rate shows an insignificant negative long-run relationship with inflation. Research limitations/implications The study uses temperature as a proxy for climate change due difficulty in obtaining accurate data for CO2 emissions. Future research should, therefore, test different other proxies and use a larger sample, to either agree with the findings or justify any deviation therefrom. Practical implications The findings of the study indicate the existence of a statistically significant long-run impact of climate change proxied by rainfall on inflation. Thus, this suggests that variations in seasonal rainfall and an escalation in weather changes diminish agricultural supply, including food products, leading to increased food prices and overall inflation. The substantial effect that climate change-related rainfall has had on South Africa’s inflation rate highlights the prerequisite for the Reserve Bank to take climate change into account when developing their monetary policy frameworks. This integration is crucial, as it serves as the initial phase for the Reserve Bank to adeptly implement their monetary policy objectives. This highlights that climate change effects are multidimensional and the fight against it should be collective. Based on this analysis, the immense pressing issue is not the lack of macro-level climate change programs or policies, but rather the establishment of effective policies that can successfully mitigate climate change in South Africa. Therefore, the Reserve Bank should take climate change into account when implementing its monetary policies. This may be attributed to the fact that, with their current macroeconomic forecasting models, it is becoming increasingly difficult for them to pinpoint the causes of inflationary pressures and create practical countermeasures as a result of climate change. They can then improve the processes by which they identify and evaluate risks associated with climate change and promptly adjust their strategies in response to alterations in the pace and severity of climate change. Originality/value The study uses recent data to clarify the relationship between inflation and climate change in the South African context using rainfall and temperatures as proxies for climate change.

Energy consumption and agricultural production are major emitters of greenhouse gasesGreenhouse gases that cause climate change. This climate change in turn affects energy sustainabilitySustainability and food security for both present and future generations. In this chapter, the Auto-Regressive Distributive Lag modelAuto-Regressive Distributive Lag Model (ARDL) and the Granger Causality testGranger causality test were used to examine the connection between climate change, energy consumption, and agricultural output in NigeriaNigeria for a duration of 28 years (1990–2017), using carbon dioxide gas as a proxy for climate change. According to the short-run ARDLAuto-Regressive Distributive Lag Model calculation, increasing energy consumption increases carbon dioxide gas emissions, whereas increasing agricultural output decreases carbon dioxide gas emissions. The long-run estimate, on the other hand, demonstrates that increasing energy usage reduces carbon dioxide emissions while increasing agricultural production does not catalyze any significant change in carbon dioxide emissions. The Granger Causality testGranger causality test results revealed that there is a bidirectional relationship between energy consumption and carbon dioxide emissions, but there is a unidirectional relationship between agricultural output and energy consumption. This implies that in order to reduce greenhouse gasesGreenhouse gases, agricultural production and energy consumption should be increased and decreased, respectively. The results of this study show that NigeriaNigeria consumes large amounts of energy, which is mainly dominated by non-renewable energy sources, which consequently results in high emissions of greenhouse gases as a result of emissions from the energy sector. In accordance with the research results, this paper puts forward a series of policy recommendations to help promote sustainable developmentSustainable development.

Due to the negative effects of natural disasters caused by climate change, the agricultural sector, ecosystems, and the economy are under serious threat. In this study, carbon dioxide emissions from fuel use in wheat, sugar beet, and potato production between 2015 and 2024 (10 years) were determined in Türkiye, and projections for 2025-2034 were examined. In calculating fuel-based CO2 emissions for wheat, sugar beet, and potato production, the total CO2 emissions from fuel and oil sources described in the Intergovernmental Panel on Climate Change were considered. The Chain Index method was used in programming the projection coefficients. For this effect, percentages of value increase and decrease over the years were calculated, and the average projection coefficients for these percentages were determined. The range of variations was found for total carbon dioxide emissions as a maximum of 1263.32 ktCO2, and a minimum of 1064.49 ktCO2 in wheat production, a maximum of 134.35 ktCO2, and a minimum of 101.29 ktCO2 in sugar beet production, and a maximum of 26.48 ktCO2, and a minimum of 18.39 ktCO2 in potato production. It is predicted that TCOE in wheat, sugar beet, and potato production will increase in Türkiye during 2025-2034. Accordingly, 13.31% increase in TCOE is predicted for wheat production, 13.31% in sugar beet production, and 13.39% in potato production. In this context, efforts can be made to raise awareness about environmental protection and sustainable agricultural methods and to encourage their widespread adoption by farmers. Environmentally friendly alternative production techniques should be planned for soil cultivation systems, planting, fertilization, maintenance, irrigation, pesticide application, harvesting, and post-harvest processing in Türkiye, and farmers should be trained in these techniques.

Agriculture production is directly dependent on climate change and weather. Possible changes in temperature, precipitation and CO2 concentration are expected to significantly impact crop growth and ultimately we lose our crop productivity and indirectly affect the sustainable food availability issue. The overall impact of climate change on worldwide food production is considered to be low to moderate with successful adaptation and adequate irrigation. Climate change has a serious impact on the availability of various resources on the earth especially water, which sustains life on this planet. The global food security situation and outlook remains delicately imbalanced amid surplus food production and the prevalence of hunger, due to the complex interplay of social, economic, and ecological factors that mediate food security outcomes at various human and institutional scales. Weather aberration poses complex challenges in terms of increased variability and risk for food producers and the energy and water sectors. Changes in the biosphere, biodiversity and natural resources are adversely affecting human health and quality of life. Throughout the 21st century, India is projected to experience warming above global level. India will also begin to experience more seasonal variation in temperature with more warming in the winters than summers. Longevity of heat waves across India has extended in recent years with warmer night temperatures and hotter days, and this trend is expected to continue. Strategic research priorities are outlined for a range of sectors that underpin global food security, including: agriculture, ecosystem services from agriculture, climate change, international trade, water management solutions, the water-energy-food security nexus, service delivery to smallholders and women farmers, and better governance models and regional priority setting. There is a need to look beyond agriculture and invest in affordable and suitable farm technologies if the problem of food insecurity is to be addressed in a sustainable manner. Introduction Globally, agriculture is one of the most vulnerable sectors to climate change. This vulnerability is relatively higher in India in view of the large population depending on agriculture and poor coping capabilities of small and marginal farmers. Impacts of climate change pose a serious threat to food security. “Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (World Food Summit, 1996). This definition gives rise to four dimensions of food security: availability of food, accessibility (economically and physically), utilization (the way it is used and assimilated by the human body) and stability of these three dimensions. According to the United Nations, in 2015, there are still 836 million people in the world living in extreme poverty (less than USD1.25/day) (UN, 2015). And according to the International Fund for Agricultural Development (IFAD), at least 70 percent of the very poor live in rural areas, most of them depending partly (or completely) on agriculture for their livelihoods. It is estimated that 500 million smallholder farms in the developing world are supporting almost 2 billion people, and in Asia and sub-Saharan Africa these small farms produce about 80 percent of the food consumed. Climate change threatens to reverse the progress made so far in the fight against hunger and malnutrition. As highlighted by the assessment report of the Intergovernmental Panel on Climate change (IPCC), climate change augments and intensifies risks to food security for the most vulnerable countries and populations. Few of the major risks induced by climate change, as identified by IPCC have direct consequences for food security (IPCC, 2007). These are mainly to loss of rural livelihoods and income, loss of marine and coastal ecosystems, livelihoods loss of terrestrial and inland water ecosystems and food insecurity (breakdown of food systems). Rural farmers, whose livelihood depends on the use of natural resources, are likely to bear the brunt of adverse impacts. Most of the crop simulation model runs and experiments under elevated temperature and carbon dioxide indicate that by 2030, a 3-7% decline in the yield of principal cereal crops like rice and wheat is likely in India by adoption of current production technologies. Global warming impacts growth, reproduction and yields of food and horticulture crops, increases crop water requirement, causes more soil erosion, increases thermal stress on animals leading to decreased milk yields and change the distribution and breeding season of fisheries. Fast changing climatic conditions, shrinking land, water and other natural resources with rapid growing population around the globe has put many challenges before us (Mukherjee, 2014). Food is going to be second most challenging issue for mankind in time to come. India will also begin to experience more seasonal variation in temperature with more warming in the winters than summers (Christensen et al., 2007). Climate change is posing a great threat to agriculture and food security in India and it's subcontinent. Water is the most critical agricultural input in India, as 55% of the total cultivated areas do not have irrigation facilities. Currently we are able to secure food supplies under these varying conditions. Under the threat of climate variability, our food grain production system becomes quite comfortable and easily accessible for local people. India's food grain production is estimated to rise 2 per cent in 2020-21 crop years to an all-time high of 303.34 million tonnes on better output of rice, wheat, pulse and coarse cereals amid good monsoon rains last year. In the 2019-20 crop year, the country's food grain output (comprising wheat, rice, pulses and coarse cereals) stood at a record 297.5 million tonnes (MT). Releasing the second advance estimates for 2020-21 crop year, the agriculture ministry said foodgrain production is projected at a record 303.34 MT. As per the data, rice production is pegged at record 120.32 MT as against 118.87 MT in the previous year. Wheat production is estimated to rise to a record 109.24 MT in 2020-21 from 107.86 MT in the previous year, while output of coarse cereals is likely to increase to 49.36 MT from 47.75 MT. Pulses output is seen at 24.42 MT, up from 23.03 MT in 2019-20 crop year. In the non-foodgrain category, the production of oilseeds is estimated at 37.31 MT in 2020-21 as against 33.22 MT in the previous year. Sugarcane production is pegged at 397.66 MT from 370.50 MT in the previous year, while cotton output is expected to be higher at 36.54 million bales (170 kg each) from 36.07. This production figure seem to be sufficient for current population, but we need to improve more and more with vertical farming and advance agronomic and crop improvement tools for future burgeoning population figure under the milieu of climate change issue. Our rural mass and tribal people have very limited resources and they sometime complete depend on forest microhabitat. To order to ensure food and nutritional security for growing population, a new strategy needs to be initiated for growing of crops in changing climatic condition. The country has a large pool of underutilized or underexploited fruit or cereals crops which have enormous potential for contributing to food security, nutrition, health, ecosystem sustainability under the changing climatic conditions, since they require little input, as they have inherent capabilities to withstand biotic and abiotic stress. Apart from the impacts on agronomic conditions of crop productions, climate change also affects the economy, food systems and wellbeing of the consumers (Abbade, 2017). Crop nutritional quality become very challenging, as we noticed that, zinc and iron deficiency is a serious global health problem in humans depending on cereal-diet and is largely prevalent in low-income countries like Sub-Saharan Africa, and South and South-east Asia. We report inefficiency of modern-bred cultivars of rice and wheat to sequester those essential nutrients in grains as the reason for such deficiency and prevalence (Debnath et al., 2021). Keeping in mind the crop yield and nutritional quality become very daunting task to our food security issue and this can overcome with the proper and time bound research in cognizance with the environment. Threat and challenges In recent years, climate change has become a debatable issue worldwide. South Asia will be one of the most adversely affected regions in terms of impacts of climate change on agricultural yield, economic activity and trading policies. Addressing climate change is central for global future food security and poverty alleviation. The approach would need to implement strategies linked with developmental plans to enhance its adaptive capacity in terms of climate resilience and mitigation. Over time, there has been a visible shift in the global climate change initiative towards adaptation. Adaptation can complement mitigation as a cost-effective strategy to reduce climate change risks. The impact of climate change is projected to have different effects across societies and countries. Mitigation and adaptation actions can, if appropriately designed, advance sustainable development and equity both within and across countries and between generations. One approach to balancing the attention on adaptation and mitigation strategies is to compare the costs and benefits of both the strategies. The most imminent change is the increase in the atmospheric temperatures due to increase levels of GHGs (Green House Gases) i.e. carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and chlorofluorocarbons (CFCs) etc into the atmosphere. The global mean annual temperatures at the end of the 20th

Background: The provision of energy services is a vital component of the energy system. This is often considered emission-intensive and at same time, highly vulnerable to climate change conditions. This forms the fundamental objective of this thesis, poised to examine technoeconomic and environmental implications of policy intervention, targeted at cushioning impacts of climate change on the energy system. Aims: Four research queries are central to this work: (1) Review literature on impacts of CVC (2) Estimate influence of seasonal climatic and socioeconomic factors on energy demand in Australia; (3) Model dynamic interactions between energy policies and climate variability and change (CVC and (4) Identify least-cost combination of electricity generation technologies and effective emissions reduction policies under climate change conditions in Australia. Methods: A systematic scoping review method was first applied to identify consistent pattern of CV&C impacts on the energy system, while spotting research gaps in studies that met the inclusion criteria. Databases consisting of Scopus and Web of Science were searched, and snowballing references in published studies was adopted. Data was collated and summarised to identify the characteristic features of the studies, consistent pattern of CV&C impacts, and locate research gaps to be filled by this study. The second study applied an autoregressive distributed lag (ARDL) model to estimate temperature sensitive electricity demand in Australia. Estimates were used with projected temperatures from global climate models (GCMs) to simulate future electricity demand under climate change scenarios. The study further accounted for uncertainties in electricity demand forecasting under climate change conditions, in relation to energy efficiency improvement, renewable energy adoption and electricity price volatility. The estimates from the ARDL model and projections from GCMs were used for energy system simulation using the Long-range Energy Alternative and Planning (LEAP) system. It considered climate induced energy demand in the residential and commercial sector, alongside linking the non-climate sensitive sector with energy supply sector. This model was vital to justifying policy options under investigation. Further, LEAP modelling analysis was extended by identifying effective emission reduction policies considering CV&C impacts. Here, the Open Source Energy Modelling System (OSeMOSYS) was used for optimisation analysis to identify least-cost combination of electricity generation technologies and GHG emission reduction policies. Whereas, in the third and final study, cost-benefit analysis and estimation of long run marginal cost of electricity were conducted, while decomposition analysis of GHGs were analysed in the third study alone. Data used in the ARDL model included socioeconomic data which includes gross state product, as well as population and electricity prices from 1990-2016. The LEAP and OSeMOSYS model as used, was dated to 2014 as the base year, while several technological (power plant characteristics, household technologies), economic (energy prices, economic growth, carbon price) and environmental (emission factors, emission reduction target) variables were used to develop Australia's energy model. Results: The literature search generated 5,062 articles in which 176 studies met the inclusion criteria for the final literature review. Australian studies were scarce compared to other developed countries. Also, just few articles made attempt to examine decarbonisation under climate change. The ARDL model estimates and GCMs simulation of future electricity demand under CV&C show that Australia had an upward sloping climate-response functions, resulting to an increase in electricity demand. However, the researcher identified an annual increase in projected electricity demand for states and territory in Australia, which calls for the need to scale up RET. The LEAP model results showed substantial impacts on energy demand, as well as impacts on power sector efficiency. Under the BAU scenario, CV&C will result in an increase in energy demand by 72 PJ and 150 PJ in the residential and commercial sectors, respectively. Induced temperature enlarges the non-climate BAU demand, which will increase threefold before 2050. Under the non-climate BAU, there is an expansion of installed capacity to 81.8 GW generating 524.6 TWh. Due to CV&C impacts, power output declines by 59 TWh and 157 TWh in Representative Concentration Pathways (RCP) 4.5 and 8.5 climate scenarios. This leads to an increase in generation costs by 10% from the base year, but a decrease in sales revenue by 8% and 21% in RCP 4.5 and RCP 8.5, respectively. The LEAP-OSeMOSYS model suggests renewables and battery storage systems as least-cost option. However, the configuration varied across Australia. Carbon tax policy was observed to be effective in reducing Australia's emission and foster huge economic benefits when compared to the current emission reduction target policy in the country. Also, renewable energy technologies increase electricity sales and decrease fuel cost better than fossil fuel dominated scenarios. Conclusions: Data from this study reveals that seasonal electricity demand in Australia will be influenced by warmer temperatures. Also, the study identified the possibility of winter peaking which is somewhat higher than summer peak demand in some states located in the southern regions of Australia. However, winter peaking is projected to decline by mid-century across the RCPs, while summer peak load is projected to increase, thereby, causing power companies to expand their generation capacity which may become underutilised. Owing to increase in cooling requirements up to 2050, policy uncertainties analysis recommend renewables to match an increasing future electricity demand. The energy model indicates that ignoring the influence of CV&C may result in severe economic implications which range from increased demand, higher fuel cost, loss in revenue from decreased power output, as well as increased environmental externalities. The study concludes that policy options to reduce energy demand and GHG emissions under climate change may be expensive on the short-run, though, may likely secure long-run benefits in cost savings and emission reductions. It is envisaged that this could provide power sector management with initiatives that could be used to overcome cost ineffectiveness of short-term cost. The modelling results makes a case for renewable energy in Australia as lower demand for energy and increased electricity generation from renewable energy source presents a win-win case for Australia.

One of the most affected economies by climate change is the agricultural sector. Climate change measured by temperature and precipitation has an impact on agricultural output, which in turn affects the economy of the sector. It is anticipated that using renewable energy will lower carbon emissions that are directly related to climate change. The main objective of this study was to evaluate the impact of carbon dioxide emissions (CO2), renewable energy usage, and climate change on South Africa’s agricultural sector from 1972 to 2021. The nexus was estimated using an Auto Regressive-Distributed Lag (ARDL) Bounds test econometric technique. In the short run, findings indicated that climate change reduces agricultural economic growth and carbon dioxide emissions increase as agricultural economic growth increases. The use of renewable energy was insignificant in the short and long run. Carbon dioxide emissions granger causes temperature and renewable energy unilateral. An ARDL analysis was performed to evaluate the short and long-term relationship between agricultural economic growth, climate change, carbon dioxide emissions and renew able energy usage. The study adds new knowledge on the effects of climate change and carbon emissions on the agricultural economy alongside the use of renewable energy which can be used to inform economic policy on climate change and the energy nexus in the agricultural sector. Study findings point to the prioritization of biomass commercialization, rural and commercial farming sector bioenergy regulations and socioeconomic imperatives research is crucial in order to promote inclusive participation in the production of renewable energy.

It is well-established that climate change can be the result of human activities that create greenhouse gas emissions, which causes the greenhouse effect and further lead to the net effect of global warming. As far as the effects of climate change to human health and outputs of economic sectors are concerned, we can expect there will be negative impacts on output and employment. The objective of this study is to investigate the climate change effects to industrial output and employment in the context of ASEAN, by focusing on six emerging economies, namely, Cambodia, Indonesia, Malaysia, the Philippines, Thailand, and Vietnam for the period of 1989–2016. We use temperature and precipitation as the proxies for climate change. We apply the bounds testing procedure proposed by Pesaran et al. (Journal of Applied Econometrics 16:289–326, 2001) to analyse the cointegration relationship and the autoregressive distributed lag (ARDL) modelling approach of Pesaran and Shin (An autoregressive distributed lag modeling approach to cointegration analysis. In: Strom S (ed) Econometrics and economic theory in the 20th century: the Ragnar Frisch centennial symposium. Cambridge University Press, Cambridge, 1999) and Pesaran et al. (Journal of Applied Econometrics 16:289–326, 2001) for the long-run and short-run relationships between industrial output and employment with the climate change variables. We found long-run relationship between climate change, industrial output and employment in all the countries analysed, except for the industrial output in Vietnam. Further, the long-run and short-run results show some similarities and variations. Our findings allow us to suggest different policy implications for long run and short run based on our results.

Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions

Gaining a comprehensive understanding of the carbon emissions cycle in the atmosphere resulting from agricultural activities is crucial for assessing its influence on environmental quality. This study used panel datasets covering the period from 1990–2022 to investigate the influence of wheat and rice production on environmental quality in the six mega agricultural provinces of China namely Anhui, Hebei, Hubei, Henan, Jiangsu, and Sichuan. Study employed several econometric approaches such as Cross-Sectional Dependency tests, unit root and cointegration tests, Panel Mean Group Autoregressive Distributed Lag (PMG-ARDL), Panel Quantile (PQ) and Panel Least Square (PLS) regression analysis for the robustness of the findings. The empirical findings of PMG-ARDL model reveal that rice production positively increases CO2 emissions in the long run. The variables fertilizers usage, agricultural water consumption and agricultural credit also have positive impact on CO2 emission in the long run. Further, short-term results reveal that all the concerned variables positively contribute to increase the CO2 emissions. The PQR results illustrate that rice and wheat production, fertilizer consumption, agricultural water usage, agricultural credit and agricultural GDP have positive and significant impact on CO2 emission across the quantiles. Additionally, PLS outcomes show positive and significant association between wheat productivity, agricultural credit, fertilizer and agricultural GDP on CO2 emissions. The Dumitrescu and Hurlin (D–H) panel causality show unidirectional association among: carbon emission → pesticides use, carbon emission → temperature, and carbon emission → agricultural GDP. A significant bidirectional causal association was found between: carbon emission ↔ rice production, carbon emission ↔ wheat production, carbon emission ↔ fertilizers use, carbon emission ↔ agricultural water use, and carbon emission ↔ agricultural credit. These findings contribute to the understanding of the drivers of CO2 emissions in agriculture and provide valuable insights for policymakers aiming to mitigate environmental impacts while promoting sustainable agriculture, resilience, financial support to encourage green technology and implement robust monitoring mechanisms to protect quality of environment and agricultural sustainability.

The atmospheric carbon dioxide emissions have increased since last few years in Afghanistan. This increasing trend in carbon emissions may cause global warming, climate change and environmental pollution. Consequently, these indicated threats may suffer human life and ecological conditions in near future. Therefore, this study examines the impacts of globalization, economic growth, population and urbanization on carbon emissions in Afghanistan using annual time series data for the period 1990 – 2020. The study used the Augmented Dickey Fuller and Phillips–Perron unit root tests, the Breusch – Godfrey serial correlation Lagrange multiplier (LM) test and the autoregressive distributed lag (ARDL) bounds test to examine the short and long-run relationship of globalization, economic growth, population and urbanization with carbon emissions. The empirical results show that globalization, economic growth and population have a significant positive short and long-run relationship with carbon emissions. While urbanization has a significant short-run negative and long-run positive relationship with carbon emissions. Based on results, it is highly recommended that government should design environment friendly policies related to globalization, economic growth, population and urbanization to reduce environmental pollution in Afghanistan.

The objective of our study is to project the impacts of climate change on rain-fed wheat and barley production in Turkey for 2070–2079 and identify their policy implications. We first estimate the wheat and barley yield and area sown functions for the Konya and Adana provinces, which have been representative wheat and barley production areas in Turkey. Most of the wheat and barley in Turkey have been produced in rain-fed conditions. Rain-fed arable land in Turkey is either planted to wheat or barley, leading Turkish farmers to make planting decisions according to the relative price of these crops. The relative price reflects the previous year’s Turkish demand for and supply of both crops, and of animal products produced by using barley as feed. As expected from the rain-fed, traditional and low input wheat and barley production in Turkey, most of the estimated yield and area sown functions for these crops have statistically significant correlation coefficients to spring heat-damage variables and drought-damage variables, as well as to the cumulative monthly rainfall variables. Iterative estimation methods were used for selecting the best correlation coefficients of these variables. These coefficients not only appropriately reflect the severe fragility in the rain-fed wheat and barley production on the rain-fed arable land in Konya and Adana, but also provide a good basis for estimating the 2070s’ wheat and barley production using the monthly temperature and rainfall projected by a regional circulation model (RCM) in our study for that period. We think that the impact of climate change on crop yield and area sown in the real world is determined not only by the crop responses, but also by farmers’ adaptations and agricultural experiment stations’ research adaptations to climate change. This is affected by the changes in demand for and supply of wheat, bread, barley and animal products, reflected in the relative price between wheat and barley in the previous year, as well as by policy and institutional changes. Our model incorporates most of these aspects explicitly and implicitly. Thus, we can conclude that the process to assess the impacts of climate change on wheat and barley production in Turkey using our model better emulates the real world process than the physiological plant growth model that focuses the impacts of climatic change on mainly the growth of wheat and barley. Consequently, we insert the 2070–2079 monthly rainfall and temperature data projected by the RCM into the estimated yield and area sown functions in order to project wheat and barley production for that period. Then, adding the FACE 2 * CO2 fertilisation effect of 13% to the projected yields, the final change rates in the wheat and barley production projected for the 2070–2079 period are −14% for wheat and −28% for barley in Konya, and −0.46% for barley and +3.5% for wheat in Adana. The projected impacts of climate change on wheat and barley production in Turkey can be calculated as weighted averages of these impacts with production weights for Central Anatolia and its peripheral region, where the regions of Konya and Adana are the typical representative areas. The impact of climate change on wheat production in Turkey is −12.06% of current production. For barley this impact is −14.39%. Given the self-sufficient wheat and barley market of Turkey, these impacts may cause a food crisis in the case of wheat, and severe shortage of feed and livestock products in the case of barley in Turkey. We suggest the development and use of new wheat and barley varieties that are resistant to spring heat damage and drought and preparation of the economic and political capabilities to import the needed wheat and barley that have been staple foods for Turkish people for thousands of years.

Egypt, the world’s second-largest wheat importer, has been working hard to narrow the gap between its domestic wheat production and consumption. However, these efforts have been hampered by water scarcity and the negative impact of climate change on wheat production. This study seeks to analyze the influence of climatic and technical factors on wheat production in Egypt over the long and short term. Using Egypt-specific data from 1961 to 2022 and employing the Autoregressive Distributed Lag (ARDL) model and Granger-causality, the study examines the impact of factors such as harvested area, fertilizers, technology, CO2 emissions, seasonal temperature and precipitation patterns (winter and spring) on wheat production in Egypt. The empirical results indicate that the harvested area, level of technology, and average winter temperature significantly and positively impact wheat production. Precisely, a 1% increase in these factors leads to a 1.08%, 1.49%, and 6.89% increase in wheat production, respectively. Conversely, a 1% rise in CO2 emissions, average spring temperature, and precipitation reduced wheat production by 1.76%, 0.52%, and 0.054%, respectively. The Granger causality results indicate a bidirectional causal relationship between wheat production and harvested area. Furthermore, the technology level exhibits a significant causal influence on wheat production, cultivated area, and CO2 emissions, highlighting its pivotal role in both the wheat production process and its environmental impact. In conclusion, this study is crucial for Egypt’s future food security. By identifying the key climatic and non-climatic factors that impact wheat production, policymakers can gain valuable insights to address climate change and resource limitations. Improving domestic production through technological advancements, effective resource utilization, and climate-resilient practices will ensure a sustainable food supply for Egypt’s expanding population in the face of global uncertainties.

Global climate change caused by Greenhouse Gases (GHGs), particularly carbon dioxide (CO2) emissions, poses incomparable threats to the environment, development and sustainability. Vietnam is experiencing continuous economic growth and agricultural advancement, which causes higher energy consumption and CO2 emissions. Understanding Vietnam’s sensitivity to climate change is becoming more crucial for governments trying to reconcile climate change mitigation and sustainable development. Analyzing pollution-development trade-offs can help minimize environmental degradation in Vietnam. Therefore, the present study empirically investigated the nexus between economic growth, energy use, agricultural added value and CO2 emissions in Vietnam. To investigate the short-run and long-run relationships between the variables, this study employed the autoregressive distributed lag (ARDL) technique and the Vector Error Correction Model (VECM) using the time series data from 1984 to 2020 for Vietnam. The empirical findings indicated that economic growth and energy use trigger environmental degradation by increasing CO2 emissions, whereas enhancing agricultural added value improves Vietnam’s environmental quality by reducing CO2 emissions in both the long-run and short-run. The estimated results are robust compared with alternative estimators such as dynamic ordinary least squares (DOLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). This research contributes to the existing literature by shedding light on the potential of agricultural added value to reduce emissions in Vietnam and provides policy recommendations in areas of low-carbon economy, promoting renewable energy, and sustainable agriculture that can reduce CO2 emissions in Vietnam.

This study employs two distinct machine learning (ML) methodologies to investigate the impact of 12 different key climatic variables on wheat production efficiency, a crucial component of the global and Turkish agricultural economy. Neural network (NN) and eXtreme Gradient Boosting (XGBoost) algorithms are utilised to model wheat production performance using climate variable data, including greenhouse gases, from 1990 to 2024. The models incorporate a total of 21 different independent variables, comprising 9 climatic variables (daytime and nighttime total 18 variables) and 3 distinct greenhouse gas variables, considering day and night values separately. Wheat production efficiency analyses indicate that between 2005 and 2024, Turkey’s wheat cultivation area decreased, while production efficiency increased. ML analyses reveal that greenhouse gases are the most influential variables in wheat production. XGBoost identified four different variables associated with wheat production, whereas the neural network determined that five different variables affect wheat production. While the influence of greenhouse gases was observed in both ML models, it was concluded that nighttime humidity, daytime 10 m v-wind, and daytime 2 m temperature may be additional climatic factors that will impact wheat production in the future. This study elucidates the complex relationship between climate change and wheat production in Turkey. The findings emphasise the importance of the potential for predicting wheat yields with the dual influence of climatic factors and informing agricultural producers about such next-generation practices.