Term Trends Research Articles

THE INDONESIA-MIDDLE EAST BUSINESS CYCLE SYNCHRONIZATION

This study aims to analyze the business cycle synchronization (BCS) between Indonesia and 14 Middle Eastern countries and its determinants. The BCS was measured using BCS Index. The panel data was analysed using Feasible General Least Square with estimated coefficients. The results show that the Indonesia-Middle East BCS is strong in terms of index and trend. Trade openness positively and significantly affects the BCS. This study provides evidence for the endogeneity of trade relations and the BCS. Price factors, such as inflation and exchange rates, are more difficult to forecast. This information allows stakeholders to promote Indonesia’s open trade policies and liberalization in the Middle East. This study proposes an update for how Indonesia and Middle East have benefited from the openness of trade and investment that exists between them. The BCS indices and trends in the two regions are important sources for expanding their economic partnership.JEL: E32, F13, N95, C33.

Scientific Radiography of Healthcare System Process Efficiency Digitalisation

Abstract Digitalisation remains a complex process in terms of integration into healthcare, a significant challenge worldwide. This study aims to identify the most influential trends in terms of authors, sources, countries, affiliations, and highly engaged documents that significantly contribute to the healthcare system’s digitalisation. To perform a comprehensive science mapping analysis, a logical data frame of 336 Web of Science database recent papers published between 2018 and 2022 are analysed using R-Bibliometrix. Our results highlighted throughout a scientific mapping and visual framework that digitalisation of the health-care system is a revolutionary, actual, and pervasive concept, considered a new research area recognised by evolution and consistent growth. Moreover, the results provide different types of networks and highlight the keywords, authors, documents, and countries with the highest interest in the subject of the digitalisation of healthcare.

Computational Modeling, Simulation and Tight–Binding Analysis of Electronic Characteristics of Defective Germanene Nanoribbons for Future Applications of High Sensitivity Ge-Based Nanosensors

In this research, the electronic characteristics of germanene sheet and nanoribbons using the computational modeling, simulation and tight binding approximation are investigated. Our analysis is focused on the pristine sheet of germanene as well as defective monolayer. The obtained results show that applying the Stone–Wales defect into the germanene monolayer changes the energy band structure. The E-k curves around the Dirac point are no longer linear, in which a band gap is opened, and the Fermi velocity is reduced. Furthermore, the main parameters such as density of states, carrier concentration in degenerate and non-degenerate limits, carrier effective mass, conductance and AC conductivity of germanene are analytically modeled with the inclusion of the spin–orbit coupling effect, temperature and ribbon width. Obtained results demonstrate that the inclusion of the spin–orbit coupling makes a small splitting of the energy levels and creating a small band gap. Finally, the Tight binding and computational values are compared with our simulation results and available data, and a rational agreement is reported in terms of trend and value. The findings of this study provide theoretical reference for the design of germanene-based nanosensors and optoelectronic devices.

Sediment Compaction in Experimental Deltas: Toward a Meso‐Scale Understanding of Coastal Subsidence Patterns

AbstractWe present the first investigation of subsidence due to sediment compaction and consolidation in two laboratory‐scale river delta experiments. Spatial and temporal trends in subsidence rates in the experimental setting may elucidate behavior which cannot be directly observed at sufficiently long timescales, except for in reduced scale models such as the ones studied. We compare subsidence between a control experiment using steady boundary conditions, and an otherwise identical experiment which has been treated with a proxy for highly compressible marsh deposits. Both experiments have non‐negligible compactional subsidence rates across the delta‐top, comparable in magnitude to our boundary condition relative sea level rise rate of 250 μm/hr. Subsidence in the control experiment (on average 54 μm/hr) is concentrated in the lowest elevation (<10 mm above sea level) areas near the coast and is likely related to creep induced by a rising water table near the shoreface. The treatment experiment exhibits larger (on average 126 μm/hr) and more spatially variable subsidence rates controlled mostly by compaction of recent marsh deposits within one channel depth (∼10 mm) of the sediment surface. These rates compare favorably with field and modeling based subsidence measurements both in relative magnitude and location. We find that subsidence “hot spots” may be relatively ephemeral on longer timescales, but average subsidence across the entire delta can be variable even at our shortest measurement window. This suggests that subsidence rates over a short time frame may exceed thresholds for marsh platform drowning, even if the long term trend does not.

240 years of Swedish terms of trade – structure, volatility, and connection to economic growth

In this paper we present new foreign trade- and price data to analyze Swedish terms of trade in the very long run, from 1780 to 2018. We examine what influenced the trend in the terms of trade in the long run, particularly in how it related to the general trade structure. We also analyze the volatility of the terms of trade, and discuss how it might have affected economic growth in Sweden. The index improved slowly but substantially over the century from 1850 to 1950, which was likely connected to another poignant trend, a steadily increasing share of manufactures and finished goods in the export basket. In comparative perspective, Sweden had more industrial exports and lower export concentration than most of the rest of the European periphery, as well as much lower terms of trade volatility. We find that the growth trend and low volatility of Swedish terms of trade was positively connected with economic growth, but only really so from about 1850 to 1913. During this period the development of the terms of trade was particularly connected to increasing investment in new industries. The years from 1914 to 1960 were on the other hand characterized by high volatility in the terms of trade, and was still connected to high growth rates, such as during the interwar period. Hence, we conclude that while low volatility was connected to the onset of Swedish industrialization, it was also possible to maintain high growth rates during periods of high terms of trade volatility.

Mapping Glacier Basal Sliding Applying Machine Learning

AbstractDuring the RESOLVE project (“High‐resolution imaging in subsurface geophysics: development of a multi‐instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass‐filtered copies of the continuous seismic waveforms, we compute energy‐based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on‐ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.

Internet of Things and Wearable Devices: A Mixed Literature Review

In recent years, the concept of the Internet of Things (IoT), its different models and emerging conceptual frameworks, have offered new opportunities for the integration of technologies into everyday life. Although initially based on the notion of the link between a physical device, its counterpart in a virtual environment and the technological aspects of that relationship, IoT-based wearable devices now offer multiple contributions to all types of users, from professionals and organizations to ordinary people. However, there are inaccuracies, abundant definitions, limited clarity in terms of trends, benefits, components, challenges and future lines of research. In this sense, a mixed literature review study was conducted. The mixed design, of dominant type (CUAN-qual), was composed of a quantitative analysis of bibliometric type and a qualitative analysis of thematic type and embedded in the first one. Data are provided on the main trends observed in the bibliometric indicators studied, a synthesis of some qualitative indicators of particular relevance and pertinence. In addition, future lines of research are presented to address strengths and challenges in this field.

Experimental and compactional studies of interface toughening on GFRP by hierarchical distribution of halloysite nanotubes

The incorporation of nano-inclusions into the fiber-matrix interfacial regions to create hierarchical structured fiber reinforced polymers (FRPs) is a novel approach aimed at enhancing mechanical properties. However, the specific toughening mechanism facilitated by the hierarchical structure has not been extensively explored or discussed in detail. This study investigates the mechanical properties of glass fiber reinforced polymers (GFRPs) that have been hierarchically incorporated with natural halloysite nanotubes (HNTs) using experimental, computational, and numerical approaches. The hierarchical distribution of HNTs is achieved through electrophoretic deposition (EPD). The stiffening effect of HNTs incorporation is computed using the random-averaged micromechanical Mori-Tanaka model. Subsequently, stiffness and stress concentration analyses are carried out on mesoscale representative volume elements (RVEs) of GFRPs with periodic boundary condition (PBC) assignments in ABAQUS. Overall, the experimental results are consistent with the numerical and computational results in terms of trend and magnitude. The amorphous HNTs (AHNTs) with larger cross-sectional aspect ratios demonstrate significant enhancement of 45 % in transverse modulus. The in-plane stiffness and strength of GFRPs with hierarchical distribution are slightly inferior to those of GFRPs with random distribution. Nevertheless, the hierarchical distribution of nano-inclusions leads to a substantial improvement in interlaminar strength and toughness, with enhancements exceeding 70 % and 100 % respectively. This can be attributed to the reinforcement and toughening effects on the fiber-matrix interface brought about by the hierarchical distribution.

Emotions and digital storytelling in the educational context: A systematic review

AbstractBoth emotions and digital storytelling (DST) have an important role in learning‐teaching processes. Emotions are also important for DST. There is currently much interest in how emotion and DST are addressed together in education, how relations are established, how they are grounded and what the outcomes are. There is a need for a systematic review study in order to find answers to these questions and to give direction to new researches. This systematic review study presents an overview of emotion and DST research trends (in terms of their demographics, research foci and issues) in an educational context, how they are theoretically grounded, and their outcomes. Seventy research articles were reviewed using a mixed‐method approach combining systematic mapping and systematic review. In the studies reviewed, DST serves emotional outcomes as a learning design, multimodal pedagogy, pedagogical method, practice or tool for students, teachers, adults and families. DST provides an emotional connection, engagement, experience and safe space; supports the development of self‐dynamics such as self‐reflection and self‐awareness of emotions; facilitates emotional sharing and empathy; and develops emotional skills in the learning‐teaching process. In addition, it is found that there are deficiencies in the theoretical frameworks of the studies. Finally, the results are discussed and directions for future studies are suggested.

Spatial organization of riverside territories in Irkutsk area: mid XX – early XXI centuries

The paper presents a design of a scientifically-based, theoretical model for organization of riverside territories of the Irkutsk area under contemporary conditions. The work set out to analyze the factors influencing the formation of the riverside territories, problems and possible directions for necessary transformations; to systematize and adapt the current theoretical and methodological base; to develop a theoretical design model for spatial organization of the territories. The research methodology involves system and landscape-geographical approaches. Urban ecological analysis of riverside territories included analysis of spatial and strategic planning documents, field investigation of the territories, graphical analysis of world experience in terms of patterns and trends in organizing riverside territories, as well as conceptual modeling with consideration to the urbanization level of the territories. The study consists in developing the following definitions: scientific ground for spatial organization of riverside territories in urban area; global trends in organizing riverside territories in urban area; theoretical design model and perspectives for transformations in the territories. The study results include the development of a theoretical design model for the riverside territories in the Irkutsk area, enabling the growth potential and organization principles of the riverside territories to be effectively identified with consideration to their urbanization level. In addition, the authors offer a definition for the concept of “riverside territories in the urban area.”

Prediction of Surface Settlement in the Cross-passage by FIG-SVM Approach

Introduction: An optimized support vector machine prediction model (SVM) with fuzzy information granulation (FIG) is established for surface prediction in this paper. Based on the measured data, cubic spline interpolation was processed and FIG approach was applied to granulate the surface settlement parameters into fuzzy particles Low, R and Up, and the particles are used to represent the range of the measured data variation. Method: For each fuzzy particle, particle swarm optimization (PSO) was used to select the best penalty and kernel function parameters to minimize the K-fold cross-validation (K-CV) error. With the optimized parameters, the prediction model was trained for the nonlinear prediction of fuzzy particles. Finally, the surface settlement trend in terms of Low, R, Up for the next construction stage can be predicted effectively based on the previous test data. Taking the surface settlement monitoring point D5-5 as an example, the error of the predicted range of surface settlement at D5-5 is 5.82%, 5.42%, and 8.0%, respectively Result: The error of other predicted points is also less than 10%, indicating the effectiveness of the prediction model. Compared with the numerical simulation results, the accuracy of the prediction model was further verified. Conclusion: Combined with the simulation method, the "simulation - prediction" patent scheme for monitoring the surface settlement of the Cross Passage is proposed in this paper. The research results indicate that the model proposed in this paper can easily and effectively predict the range and trend of changes in surface settlement, and is suitable for practical engineering applications.

An Analysis of CSR and its Expenditure in India in the Terms of Trends, Impact and Challenges

The United Nations Industrial Development Organisation (UNIDO) has defined corporate social responsibility (CSR) as "a management concept whereby companies integrate social and environmental concerns in their business operations and interactions with their stakeholders. CSR is a way in which companies achieve a balance of economic, environmental and social imperatives". Corporate Social Responsibility (CSR) has emerged as a crucial aspect of business operations worldwide, with companies recognizing their responsibility to contribute positively to society. In India, the CSR landscape has witnessed significant growth and transformation since the introduction of the Companies Act, 2013, mandating qualifying companies to allocate a portion of their profits towards CSR activities. This research paper delves into the CSR expenditure in India, aiming to analyze the trends, impact, and challenges faced by corporations in fulfilling their CSR commitments. The study employs both qualitative and quantitative research methods, including data analysis, case studies, and interviews with key stakeholders to gain comprehensive insights into the subject matter. The paper sheds light on the effectiveness of CSR initiatives and provides valuable recommendations for enhancing CSR practices in India. Since the enactment of CSR provisions in April 2014, companies have spent nearly 1.27 trillion rupees or 1.27 lakh crore rupees in a span of seven years as per the data available on the National CSR Portal based on the disclosures made by companies. This money has been spent across 29 different sectors such as health, education, environment, welfare, development, and others. From over Rs. 10,065 crores spent in 2014-15, the CSR expenditure in India has increased to Rs. 25,715 crores in 2020-21 registering a 2.5-times increase in the seven years of implementation. With nearly Rs. 36,815 crores, the education sector received 29% of the CSR expenditure between 2014-15 and 2020-21. The health sector comes next with 20% of the CSR expenditure of Rs. 25,391 crores. More than Rs. 12,300 crores were spent in Rural development projects which accounted for 9.7%. The three sectors are the only ones to receive more than Rs. 10,000 crores each of CSR expenditure and together accounted for nearly 59% of the total CSR expenditure incurred in the country in seven years. Environmental sustainability, malnutrition, hunger and poverty, livelihood enhancement projects, central government funds including PMNRF, sanitation, art & culture, and vocational skills received more than Rs. 2000 crores each and together contributed to another 29.4% of the expenditure. Safe drinking water, women empowerment, natural resources conservation, gender equality, animal welfare, orphanages, armed forces (veterans, war widows, etc.), special education, SwachchBharathKosh, sports, technology incubators, senior citizens welfare, agroforestry, slum area development, and socio-economic inequalities are the other sectors in which CSR amount is used. These sectors accounted for <9% of the CSR expenditure. For the remaining expenditure (~3%), companies have not disclosed the details of the projects.

뇌 기반 학습의 국내 연구 동향 분석

Objectives The study aims to analyze the trends of research in brain-based learning in the domestic context and to provide implications for future research tasks and direction.
 Methods 97 papers with the keywords “brain education” and “brain learning” published in education journals listed in the Korean Citation Index (KCI) journals from 2003 to April 2023 were collected. Among them, 66 papers that were appropriate for the research purpose were selected as the final analysis target. The analysis criteria were research year, research purpose and content, research method and data collection method, and research subject. Content analysis was conducted.
 Results First, the results of the study showed that research on brain-based learning has been actively studied since the mid-2010s. Second, the results of the study showed that the highest proportion of research on brain- based learning (43.9%) was related to understanding. Third, the trends in research methods showed that qual-itative research methods (47.0%) and quantitative research methods (43.9%) were used in similar proportions, and mixed research methods were used in the lowest proportion of 6 (9.1%). The most common data collection method was documents with 28 (42.4%). Finally, in terms of research trends by research subject, there were many studies on infants and young children, and adult teachers also had a lot of research on infant teachers.
 Conclusions This study suggests that detailed policy support and educational discussions are needed to promote the activation of brain-based learning research. Furthermore, future research should involve the active use of ad-vanced devices such as EEG or fMRI to directly validate the brain's learning processes, moving beyond the evalua-tion of program effectiveness.

The Nexus Between Construction Contract Risk Assessment and Big Data in China

The construction market in China is currently showing an increasing trend in terms of project size, complexity, and technical difficulty. With large initial investments and long capital payback periods, construction projects are vulnerable to the external environment during development and risk control is particularly important. Contracts are at the heart of project management and every step of project development revolves around the content of the contract signed by the contractor. Signing contracts to protect the rights of both parties has become the most effective way of controlling project risk. And with big data technology being rapidly developed and widely used, and the amount of data created and accumulated by the internet growing exponentially, big data has the potential to be used for analysis, forecasting and decision making in the construction industry. The aim of this study is therefore to develop a risk identification list for construction project contracts in China, to score the risks within the list according to their incidence and importance, and to find practical applications of big data technology in contract risk assessment. To achieve this goal, this study used qualitative research and in-depth interviews with experts within the Chinese construction engineering industry. The results of this study form a list of risks in Chinese construction contracts, indicating that late payment and government intervention are the most important risks in Chinese construction contracts. This study also identifies TianYanCha, a commonly used big data enterprise risk identification platform in China, which provides big data support in three areas: evaluating owner integrity, enterprise risk identification, and owner relationship knowledge. At the practical level, this study helps contractors to assess project risks and improve contractual contents during the preparation phase of construction projects, thus improving the success rate of projects. At the theoretical level, this study fills the gap in contractual risk assessment for construction projects in China and provides insights for subsequent risk assessment of construction projects based on the contractor's perspective.

Assessment of Ocean Temperature Trends for the Scotian Shelf and Gulf of Maine Using 22 CMIP6 Earth System Models

ABSTRACT This study examined the results of 22 CMIP6 (Coupled Model Inter-comparison Project phase 6) Earth System Model (ESM) simulations for four regions on the Scotian Shelf and Gulf of Maine. A comparison between the historical simulations from the CMIP6 ESMs with observational sea surface and bottom temperature (SST, BT) data demonstrates that the eddy-permitting ESMs do not perform better than coarse-resolution non-eddy permitting models in terms of long-term trends. Eddy-permitting ESMs reduce model SST bias but not BT bias. In general, the 22 CMIP6 ESMs show limited skill for historical BT simulations in these shelf regions. Climate projections under ssp (Shared Socio-economic Pathways)245 and ssp370 for the 2020–2049 period suggest that the largest seasonal SST increase will occur in summer for both the Scotian Shelf and the Gulf of Maine. Under both climate scenarios, the SST of the Scotian Shelf (Gulf of Maine) increases by 1.2–1.8 °C (1.4–1.7 °C) for the 2040–2049 period relative to 1995–2014, and bottom temperature increases by 1.2–1.6°C (1.3–1.4 °C) for the same period. For SST, five models exhibit abnormally warm projections. The ESMs’ performance against observations suggest the SST changes are probably underestimated, while the BT changes are likely overestimated.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

Global Research on International Students’ Intercultural Adaptation in a Foreign Context: A Visualized Bibliometric Analysis of the Scientific Landscape

As the volume of research on intercultural adaptation of international students (IAIS) increases significantly over the past decades, a systematic review of the diverse literature on a global scale and its development process becomes essential. This study conducted a thorough bibliometric analysis for the knowledge domain of IAIS research using relevant articles ( N = 1,126) from 1990 to 2022 in Thompson Reuter’s Web of Science (WoS) database-Core Collection. This work aims to analyze the advances and trends on the subject using a set of bibliometric analytical tools, including Bibliometrix, VOSviewer, and BibExcel. The research findings demonstrate that the number of published articles projects an ostensible upward trend in terms of temporal distribution, especially since 2007. The USA occupies a leading position in terms of the largest publication output and a greater influence therewith. The most productive journal, author(s), and institution(s) are International Journal of Intercultural Relations, Brown L and Wang KT, and the University of Hong Kong and the University of Queensland, respectively. Collaboration in this field has become mainstream but still needs to be enhanced among researchers and at national and institutional levels. The frequency and co-occurrence analysis of keywords reveals that the main keywords for discussion include international students, adjustment, acculturation, adaptation, acculturative stress, identity, social support, stress, experiences, and depression, while cultural intelligence and intercultural sensitivity, culture shock and intercultural challenges, and integration and migration serve as the emerging themes. The study would aid academic researchers and policymakers in understanding the intricacies of IAIS and identifying areas that require further investigation.

The Stepwise Reduction of Multiyear Sea Ice Area in the Arctic Ocean Since 1980

AbstractThe loss of multiyear sea ice (MYI) in the Arctic Ocean is a significant change that affects all facets of the Arctic environment. Using a Lagrangian ice age product, we examine MYI loss and quantify the annual MYI area budget from 1980 to 2021 as the balance of export, melt, and replenishment. Overall, MYI area declined at 72,500 km2/yr; however, a majority of the loss occurred during two stepwise reductions that interrupt an otherwise balanced budget and resulted in the northward contraction of the MYI pack. First, in 1989, a change in atmospheric forcing led to a +56% anomaly in MYI export through Fram Strait. The second occurred from 2006 to 2008 with anomalously high melt (+25%) and export (+23%) coupled with low replenishment (−8%). In terms of trends, melt has increased since 1989, particularly in the Beaufort Sea, export has decreased since 2008 due to reduced MYI coverage north of Fram Strait, and replenishment has increased over the full time series due to a negative feedback that promotes seasonal ice survival at higher latitudes exposed by MYI loss. However, retention of older MYI has significantly declined, transitioning the MYI pack toward younger MYI that is less resilient than previously anticipated and could soon elicit another stepwise reduction. We speculate that future MYI loss will be driven by increased melt and reduced replenishment, both of which are enhanced with continued warming and will one day render the Arctic Ocean free of MYI, a change that will coincide with a seasonally ice‐free Arctic Ocean.

Long term trends and variabilities of rainfall of the global monsoon systems during boreal and austral summer seasons

Long term trends and variabilities of rainfall of the global monsoon systems during boreal and austral summer seasons

Long Term Trends in Rainfall and Temperature Effects on Food Security in Pakistan: An Analysis of 75 Years (1947-2021)

This study investigates the rainfall and temperature impact on food security (Wheat production) in Pakistan. The data nature is quarterly, and the time period is from 1947 to 2021. Econometrics approach simple OLS used. The wheat production is based on January, March, and November rainfall and temperature. In the findings of model 1, the rainfall in January and wheat production are negatively correlated. Besides, temperature and wheat production are directly correlated with each other. In Model 2, the rainfall has a significant and positive impact on wheat production. In the same month, the temperature was insignificant. The combined effect of rainfall and temperature has a negative impact on wheat production. It suggests that the combined effect of March rainfall and March temperature has a significant impact on wheat production at 10%. In model 3, November rainfall and wheat production are negatively correlated. The combined impact of November rainfall and November temperature has a positive and significant impact on the dependent variable. The study suggested the government reduce CO2 emissions in various sectors as well as improve technology and hybrid seeds. Besides, the state also adopts long-term reduction policy such as other developing countries adopts.