The Proceedings of The International Conference on Climate Change

CENTRAL AMERICAN COFFEE FARMERS´ MIGRATION TO THE USA: AN ANALYSIS OF THE MULTICAUSAL NATURE OF CLIMATE-DRIVEN MIGRATION

G Ferrucci — Mon, 12 Jun 2023 00:00:00 +0530

Unauthorized migration from Honduras, Guatemala, and El Salvador to the United States has become a major worldwide problem in the last 10 years. To disentangle the causes of recent Central American migration, this paper's research on the multi-causal nature of population migrations takes into account economic development, political instability, violence, and crime’s impacts, and quickly changing gender roles. Drought has lately been researched as a major driver of migration in Central America's Dry Corridor by researchers and politicians, notably among coffee farmers in the region who have been badly impacted by extreme heat and lack of rain. Climate change intensifies the effects of natural disasters, thus acting as a force multiplier. Using secondary sources, including government reports, non-governmental organizations’ briefs, and academic papers to analyze Salvadoran, Guatemalan, and Honduran coffee production, the purpose of this paper is to look into the influence of climate change as a stimulus for migration. Thousands of individuals who rely on coffee cultivation have already had their livelihoods destroyed and have been forced to migrate. With rising temperatures in the region, which is one of the most sensitive to the effects of climate change, the future is unknown. The study begins with an examination of the climate change-migration nexus, which is followed by an examination of the impacts of climate change on coffee production in this region. This paper will discuss how climatic factors along with political instability and the impact of violence explain recent Central American migration behavior to progress toward a comprehensive explanation of climate-driven migration

POWER SYSTEMS RESILIENCE ENHANCEMENT: A REVIEW OF OPERATIONAL ENHANCEMENT APPROACHES

JN Chivunga, Z Lin, RE Blanchard — Mon, 12 Jun 2023 00:00:00 +0530

Power system resilience enhancement (PSRE) describes approaches used in reducing the extent of direct impacts caused by climate change and approaches in quickly restoring the grid functionality to its usual status. Recently, much research has been conducted on PSRE approaches to the effects of climate change. There are abundant review papers on PSRE to the impacts of climate. But there are no significant amounts of review papers focusing on operational resilience enhancement approaches on the entire power system, particularly with the focus and classifications suggested in this paper. Operational resilience-oriented modelling approaches and strategies have been comprehensively reviewed across five distinct dimensions including operational strategy, enhancement type, hierarchy, modelling methods and model formulation. A standard literature review process was followed to identify articles, with the initial search covering PSRE in general. Out of these, papers focusing on operational resilience enhancement were selected. The selected papers were classified according to the operational enhancement approaches, enhancement types, modelling methods, model formulation and the enhanced hierarchy criteria. The operational approaches, which include distributed energy resources, microgrids, preventive control, response, design standards upgrade, advanced visualisation and situational awareness systems and advanced and adaptive restoration operational techniques, were reviewed. The study reveals that for a single operational approach, different enhancement techniques can be employed using different modelling techniques. However, limited studies have so far explored an entire grid improvement as opposed to transmission and distribution structures. In addition, among the optimisation, probabilistic, simulation and AI based modelling methods, optimisation approaches have been extensively used. There are very limited studies that combine these modelling approaches in one study. Research gaps and directions for future research have been suggested in this paper.

TREND ANALYSIS OF GREENHOUSE GASES (GHGs) EMISSIONS OVER NIGERIA FROM 2000 - 2021

AC Salihu, M Musa, NN Ubachukwu, AM Mshelia — Mon, 12 Jun 2023 00:00:00 +0530

The global climate is changing with attendant devastating consequences on human livelihood and socioeconomic activities. This change has been related to greenhouse gas (GHG) emissions, which primarily include carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) and are characterised by spatiotemporal fluctuations over the planet. The objective of the research is to analyse temporal variation of greenhouse gases emissions over Nigeria from 2000 - 2021. Data used were obtained from the archive of the Emissions Database for Global Atmospheric Research (EDGAR). This database provides time-series emissions as national totals using international statistics and a consistent Intergovernmental Panel on Climate Change (IPCC) methodology. Monthly data for a period of 21 years covering 2000-2021 were obtained for Nigeria. Trend analysis was carried out on a monthly, seasonal and annual basis to define the historical emissions trend path over Nigeria using descriptive statistics and the Mann-Kendall test. Results of the monthly analysis from January to December for the given historical years demonstrated steady upward trends for two gases namely CO₂ and N₂O which are significant at the 0.05 alpha levels. On the contrary, the CH₄gas emission indicated different trends for different months which are not significant at the 0.05 degree of alpha. Furthermore, similar scenarios of significant upward trends of CO₂ and N₂O emissions, but not for CH₄are noticeable with respect to the seasonal and annual time-series. As a result, Nigeria's Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change (UNFCCC) have not resulted in a positive commitment to achieve net-zero emissions by 2060.

CLIMATE RISK PERCEPTIONS AMONG THAI FARMERS: CASE OF RICE AND DURIAN FARMERS

C LaCroix, S Visetnoi — Mon, 12 Jun 2023 00:00:00 +0530

Climate risks, such as floods and droughts, pose a threat to farmers as their livelihood relies on their crop yields. The purpose of this research is to evaluate cash crop and high-value crop farmers’ risk perception towards climate risks, as well as their ability to cope and deal with these risks in Ayutthaya and Chanthaburi Province, Thailand. This study hypothesized that different crop types, farming regions, and socio-economic factors, may play a role in differing risk perceptions in the two groups of farmers. A sample of 100 farmers were chosen from each province via a cluster and purposive sampling technique. Quantitative analysis was used to collect primary data using questionnaires for each group. The findings showed age of durian farmers being younger, with 35% under the age of 30 and 65% of rice farmers 50 or older. Education levels varied greatly as 83% of rice farmers’ highest education completed secondary school, while 38% of durian farmers obtained a bachelor’s degree or higher. Over 50% of each group’s annual household income was under 100,000 baht (~3,000 USD), with 10% more durian farmers making over 400,000 baht (~11,000 USD). Data showed that both groups perceive floods and droughts as major risks. According to the survey results, high knowledge and dread level of climate risks were indicated. Regression analysis results determined age and education statistically significant as factors determining risk perception. This study can provide policymakers, agricultural organizations, and future researchers to be able to implement the most effective strategies to protect the farmer, such as infrastructure grants, crop insurance protection plans, and disaster response education.

VARIANTS OF RECURRENT NEURAL NETWORK MODELS FOR REAL-TIME FLOOD FORECASTING IN KELANI RIVER BASIN, SRI LANKA

C Subramaniyam, RLHL Rajapakse — Mon, 12 Jun 2023 00:00:00 +0530

The rapid advancement in computer technology has supported flood forecasting, especially neural networks (NN), an application of data-driven models. However, prediction reliability is compromised due to the data manipulation strategies and the length of the predictive horizon, especially the one-month horizon, which is ample for pre-flood management. Therefore, six (06) variants of recurrent neural networks (RNN) such as Long- and Short-Term Model (LSTM), Gated Recurrent Unit (GRU), Stacked Bidirectional and Unidirectional LSTM (SBU-LSTM), SBU-GRU, Convolution Neural Network LSTM (CNN-LSTM) and CNN-GRU, were developed for the Kelani River Basin to validate their applicability in encouraging the accuracy of monthly flood forecasting by adapting a proper data manipulation technique. Initially, climatic, and physiographic factors of the basin, where the social and economic values are grievously interrupted by frequent floods, were gathered for the study. Then, the hydrological and data science cleansing strategies were adapted to enhance the quality of the data. Besides, a Box-Cox transformation was implemented to redistribute the hydrological data into a Gaussian form to remove the significant deviation between higher and lower values. Next, grid analysis was conducted using statistical tools to quantify the performance, while the influence of data handling and model architecture was examined using uncertainty and sensitivity analysis. LSTM, GRU, SBU-LSTM, SBU-GRU, CNN-LSTM, and CNN-GRU expressed nearly 81%, 81%, 83%, 83%, 76%, and 62%, respectively, for the coefficient of determination (R2) which measures how well the forecasted values fit with the actual values. SBU-LSTM and SBU-GRU interpreted similar behavior to LSTM and GRU; however, the pattern was different in CNN-LSTM and CNN-GRU. Specifically, simple variants LSTM and GRU provided satisfactory results for the uncertainty and sensitivity analysis categories.