Global climate change has become one of the biggest challenges facing humanity in the 21st century. Climate change phenomena such as the increase in the average temperature of the Earth, the increase in the intensity of natural disasters, and rising sea levels have had a significant impact on the environment, society and the global economy [2]. Climate change has broad and complex impacts, including changes in weather patterns, droughts, floods, loss of biodiversity, and changes in ecosystems. To meet this challenge, a deep understanding of climate change and the ability to predict future changes is essential. Climate change research and modelling have been a major focus of the scientific community and international institutions over the past decades. Complex climate models have been developed to understand the interacting dynamics of the atmosphere, oceans, and environment. Although these models help in understanding climate change and evaluating future scenarios, they still face challenges in accurately reflecting the complexity of the Earth system. Therefore, the development of a more effective and accurate quantitative methodology is crucial in efforts to understand and predict global climate change. In recent decades, the increasing availability of global climate observation data and advances in computational technology have enabled the development of more sophisticated quantitative methods for climate analysis and prediction. The quantitative methodology offers a structured and mathematical approach to understanding the complex relationships between various climate variables, such as temperature, atmospheric pressure, humidity and wind [1]. Patterns. This method involves statistical analysis, mathematical modelling, and data processing to find patterns, trends, and causal relationships in climate data. One of the key challenges in developing a quantitative methodology for predicting climate change is dealing with the large dimensions and complexity of climate data. Climate data spans a wide range of time and space scales, from daily and seasonal data to long-term data spanning decades. In addition, this data is obtained from various sources, including measuring stations, satellites, and climate model simulations. Therefore, efficient and reliable quantitative techniques are needed to process, integrate and analyse climate data from these various sources. In addition, quantitative methods must also address the uncertainties associated with climate change and its predictions. Climate change is influenced by a variety of factors, including human activities, natural phenomena, and the complex interactions between Earth systems. Because of this, it is important to identify and model uncertainties in climate analysis and predictions to provide reliable information for policy makers, researchers and the general public [3]. Previous studies have tried to use various quantitative methods to predict global climate change, including regression analysis, time series analysis, machine learning, and complex climate models. However, there is no single approach that can accurately cover all aspects of climate change. Therefore, it is necessary to develop an integrated and adaptive quantitative methodology to predict global climate change more efficiently and precisely. This research aims to develop and test a new and innovative quantitative methodology for predicting global climate change. It is hoped that this method will overcome the challenges faced by previous approaches and improve our understanding of climate change and provide better guidance for mitigation policies and actions. Thus, this research is expected to contribute to global efforts to face the challenges of climate change and maintain the sustainability of planet Earth for future generations.

Global Climate and Its Impacts

Global climate change is a complex phenomenon and has wide-ranging impacts on the Earth and all living things on it. This phenomenon occurs due to an increase in the concentration of greenhouse gases in the atmosphere, such as carbon dioxide (CO2), methane (CH4), and nitrogen oxides (N2O), which are caused by human activities, especially burning fossil fuels, deforestation, and the agricultural industry. This accumulation of greenhouse gases causes an increase in the absorption of heat radiation from the Sun in the atmosphere, thus causing an increase in global temperatures. One important indicator in understanding climate change is the increase in global average temperature. According to a report by the National Center for Climate Control and Investigation (NCDC), since the mid-19th century, Earth's average temperature has increased significantly and continues to increase. This global warming phenomenon causes detrimental effects, such as the melting of polar ice caps, rising sea levels, and extreme changes in weather patterns. Changes in weather patterns include an increase in extreme weather events such as floods, droughts, tropical storms and other events. The impacts of climate change are also being felt in the natural environment, including loss of habitat, shifts in species distribution, and reductions in biodiversity. Disruption to these ecosystems can have serious consequences for animal and plant life, including threats to certain species and vulnerable ecosystems. In addition, climate change also has significant economic and social impacts. Extreme changes in weather patterns can cause economic losses due to damage to infrastructure, decreased agricultural production, and disruption to energy supply chains. On a societal scale, climate change can cause forced migration, conflicts over resources, and public health problems caused by the spread of new disease vectors. It is important to understand global climate change carefully and accurately, because its serious impacts require appropriate mitigation and adaptation efforts. Accurate predictions of future climate change are the key to planning and implementing effective actions in dealing with climate change and reducing its risks. In this section of the literature review, various studies and the latest scientific studies regarding the phenomenon of global climate change, the methods of climate prediction that have been used, and the latest efforts to deal with the impacts of climate change will be reviewed. This review will provide a theoretical foundation and in- depth understanding for the development of more efficient and accurate quantitative methodologies in predicting future global climate change.

The Role of Climate Change Prediction

Climate change prediction is very important in facing the challenges of global climate change impacts. Accurate predictions enable policy makers, researchers and communities to plan and implement appropriate mitigation and adaptation strategies. By understanding impending climate change, we can take proactive steps to reduce greenhouse gas emissions, mitigate their impacts, and increase climate change resilience.

1. Complex Climate Models

Complex climate models are tools used by climate scientists to predict future climate change. This model is based on physics and mathematics, and includes various components of climate, such as the atmosphere, oceans, ice and biosphere. Climate models make use of complex differential equations to represent the dynamics of atmospheric physics and oceanography, as well as interactions with other systems. This model also takes into account the natural and human factors that influence climate change.

2. Regression and Time Series Analysis

In addition to complex climate models, regression and time series analyses are also used to predict climate change on a local or regional scale. Regression analysis is a statistical method used to identify the relationship between two or more variables, such as the relationship between greenhouse gas concentrations and average temperature. Time series analysis, on the other hand, is a method for analyzing data sorted in time sequence to identify patterns and trends.

3. Machine Learning

Machine learning, including techniques such as regression algorithms, decision trees, and neural networks, has become popular in climate change analysis and prediction. This technique takes advantage of the computer's ability to "learn" from data and identify complex, non-linear patterns. Machine learning can be used to process and analyses climate data from multiple sources, as well as identify patterns and trends that traditional quantitative methods may be difficult to detect. In an effort to face the challenges of global climate change, it is important to develop and apply effective quantitative methods to support appropriate policies and mitigation actions. The combination of complex climate models, regression and time series analysis, and machine learning techniques can provide more comprehensive and accurate insights into future climate change. The integration of these methods will help increase our understanding of climate change and enable us to meet the global climate change challenge more efficiently and effectively.

This study uses a descriptive qualitative research approach to predict global climate change. A qualitative approach was chosen because this research aims to identify and view climate descriptively and predict future climate change which will have an impact on a sustainable economy with secondary data sources in the form of documentation data, books, articles and journals.

1. Discussion of Research Results in the Context of a Global Climate

The purpose of developing this model is to predict future climate change based on historical climate data and the results of the quantitative analysis that has been carried out. The development of a climate prediction model involves several important stages which are explained as follows:

Selection of Climate Variables:

The initial stage in model development is the selection of relevant climate variables. Selection of the right climate variable is the key to formulating accurate and meaningful predictions of climate change. The climate variable chosen must include important aspects of climate that contribute significantly to global climate change, such as air temperature, rainfall, greenhouse gas concentrations, and the area of polar ice cover.

Quantitative Relationship Modelling:

After selecting climate variables, the next step is to model the quantitative relationship between these climate variables. The use of regression analysis, time series analysis, and machine learning techniques helps identify patterns and mathematical relationships between climate variables. The developed mathematical model will become the basis for formulating future climate change predictions.

1. Integration of Observation Data and Climate Model Simulation:

It is important to integrate historical climate observation data with climate model simulation data. The integration of this data allows the predictive model to take advantage of the nature of the data that has been observed and the dynamics of the climate system predicted by the model. Thus, predictions of future climate change will reflect changes that occur based on observational data and simulation models.

Model Validation:

After the climate prediction model has been developed, this model will be validated using existing historical climate data. The validation process will test the accuracy of the model in reproducing existing observational data. The results of the validation will show the extent to which the model successfully represents the observed data correctly and the extent to which the model can be relied upon to make future climate predictions.

Model Adjustment and Optimization:

The results of model validation will be used to adjust and optimize the prediction model. Model adjustments were made to improve prediction accuracy and reliability. The process of optimizing the model involves adjusting the parameters and improving the model to achieve more accurate predictions and approach existing observational data.

Out-of-Sample Testing:

In addition to validation, the model will also be tested using out-of-sample data or data that has not been used in previous development and evaluation. Out-of-sample testing aims to evaluate the performance of the model in real-world situations that are different from the data that has been used before. This validation will provide additional information about the extent to which the model is reliable and applicable under different conditions.

The development of accurate and valid climate prediction models is a critical step in efforts to understand and face the challenges of global climate change. By using a quantitative approach and integrating data from various sources, it is hoped that the results of this research will make a significant contribution to understanding climate change and inform appropriate policies and mitigation actions for the future.

Model performance evaluation aims to measure the accuracy and reliability of the model in predicting future climate change. This evaluation is important to ensure that the prediction models that have been developed are reliable and provide accurate results in facing the challenges of global climate change.

Accuracy Testing:

Accuracy testing is the first step in model performance evaluation. In this test, the model prediction results will be compared with existing climate observation data. This method will provide information about the extent to which the model can reproduce observation data correctly. The results of the accuracy test will be shown in statistical form such as the average error and prediction accuracy.

Reliability Testing:

In addition to accuracy, reliability testing is also a focus in evaluating model performance. This test will assess the model's response to variations in climate phenomena and examine the extent to which the model can predict significant changes in climate extremes and trends. Thus, the reliability test provides information about the extent to which the model is reliable in predicting climate change.

Sensitivity Testing:

Sensitivity testing will be carried out to assess the model's response to the various parameters and assumptions used in model development. This test helps identify the key parameters that most influence the prediction results. By understanding the sensitivity of the model, researchers can identify areas that need improvement or further consideration in model development.

Uncertainty Analysis:

Uncertainty analysis becomes an important part of model performance evaluation. In this analysis, the researcher will identify and measure the uncertainty in the prediction results. This involves an analysis of the uncertainty of the input data, model parameters and climate variability. The results of the uncertainty analysis will provide awareness about the degree of uncertainty in the prediction results and help inform decision making based on the model's predictions.Out-of-Sample Validation:

Out-of-sample testing is also carried out to evaluate model performance in real-world situations that are different from the data that has been used in model development. This validation provides additional information about the extent to which the model is reliable and applicable under different conditions. Out-of-sample testing can test a model's ability to predict future climate change based on data that has not been used before. By evaluating the performance of a comprehensive climate prediction model, it is hoped that this research can produce an accurate and reliable model for predicting future global climate change. Evaluation of the performance of this model will provide better guidance for the use of prediction results in policy decision-making and mitigation efforts to face global climate change challenges. Careful evaluation of model performance will help identify model weaknesses and provide a foundation for the development of better models in the future.

Sustainable Economy

Sustainable development is a development concept that does not only focus on meeting current needs, and without reducing, sacrificing or destroying the environment. This is done so that future generations still get a decent environment to live in. The process of sustainable development can be seen from the development of land, cities, businesses and communities that aim to meet present needs without compromising meeting the needs of future generations. This concept has undergone two revolutions, namely in the 1960s to 1970s and the second revolution which occurred around 1987 under the leadership of Norwegian Prime Minister Gro Harlem Brundtland [1]. The Sustainable Development Goals contain a development pattern that includes the 3 main pillars of social, economic and environmental development as well as the pillars of law and governance. The problem of natural resource and environmental crises can be interpreted as a governance crisis that can make the economy unsustainable [2]. The fact is that economic development is dominant without integrity with ecological maintenance which has a significant impact on environmental damage. As an effort to integrate economic and ecological development, an approach called ecological modernization was created with the assumption of reducing waste and pollution through technological improvements, improved regulations and markets that are pro-ecological and green social and corporate values and practices. Some examples of ecological modernization movements are the implementation of car free days, anti-plastic bags and earth hour campaigns. Some of the requirements that must be met in the implementation of sustainable development are managing natural resources wisely, paying attention to the disposal of waste from the processing of these resources, utilizing natural resources with environmentally friendly technologies and using natural resources efficiently. One of the factors that can affect the implementation of a sustainable economy is that there are still pros and cons in society. One of the supporting factors is the availability of abundant natural resources, qualified human resources in the field of development, legal certainty, level of community participation and awareness. The principles that must be implemented in a sustainable economy are efficiency, mobility, accessibility, safety and comfort, community participation, emission restrictions, natural resources, habitats and ecosystems. These principles are grouped into economic, social and environmental aspects to prevent various types of pollution arising from pollution, loss of water catchment areas and loss of green open land.

This research shows that the quantitative research approach and the development of climate prediction models are effective approaches in understanding and predicting global climate change. By using quantitative methods, statistical analysis, and machine learning techniques, a climate prediction model has been developed to identify the mathematical relationship between climate variables and formulate predictions of future climate change. The use of historical climate observation data, satellite data, and climate model simulation data allows the integration of diverse resources to increase the accuracy and reliability of predictive models. The results of the model performance evaluation show that this model is able to reproduce observational data well and has an adequate level of accuracy and reliability in predicting future climate change. An accurate and reliable climate prediction model resulting from this research has the potential to be a valuable tool in efforts to protect the environment and reduce the negative impacts of climate change on people and ecosystems. However, it should be remembered that climate predictions are subject to a degree of uncertainty, and understanding this uncertainty must be an important part of decision-making and strategic planning in dealing with future climate change. Therefore, further research and regular updating of data are needed to continue to improve the accuracy and precision of global climate change predictions. As an effort to integrate economic and ecological development, an approach called ecological modernization was created with the assumption of reducing waste and pollution through technological improvements, improved regulations and markets that are pro-ecological and green social and corporate values and practices.

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