Introduction

Climate change has become one of the most pressing issues of our time, with its effects being felt around the world. Scientists have established that the Earth's temperature has increased by 1°C above pre-industrial levels, and this is primarily due to human activities such as burning fossil fuels, deforestation, and industrial activities. The purpose of this article is to examine the factors that have contributed to recent climate change, including natural factors and human activities.

Natural Factors of Climate Change

The Earth's climate has changed naturally over millions of years. Some of the natural factors that have contributed to climate change include:

Solar Radiation

The sun's energy is the primary source of heat and light for the Earth. Changes in the amount of solar radiation received by the Earth can cause climate change. For example, when the sun's energy output is low, the Earth receives less heat, which can lead to a cooling of the planet. Conversely, when the sun's energy output is high, the Earth receives more heat, which can lead to a warming of the planet.

Volcanic Activity

Volcanic eruptions release large amounts of gases and particles into the atmosphere, which can affect the Earth's climate. When a volcano erupts, it releases carbon dioxide, sulfur dioxide, and other greenhouse gases into the atmosphere. These gases trap heat in the Earth's atmosphere and can cause a warming of the planet. Volcanic activity can also release large amounts of ash and other particles into the atmosphere, which can reflect sunlight back into space and cause a cooling of the planet.

Changes in the Earth's Orbit

The Earth's orbit around the sun changes over time, which can affect the amount of solar radiation received by the planet. These changes in the Earth's orbit are called Milankovitch cycles and occur over thousands of years. Milankovitch cycles include changes in the tilt of the Earth's axis, the shape of the Earth's orbit, and the precession of the equinoxes. These changes can affect the amount and distribution of solar radiation received by the Earth and can cause changes in the Earth's climate.

Human Activities that Contribute to Climate Change

Human activities are the primary cause of recent climate change. The burning of fossil fuels, deforestation, and industrial activities have all contributed to the increase in greenhouse gases in the Earth's atmosphere. Some of the human activities that have contributed to climate change include:

Burning of Fossil Fuels

The burning of fossil fuels such as coal, oil, and gas releases carbon dioxide and other greenhouse gases into the atmosphere. These gases trap heat in the Earth's atmosphere and cause a warming of the planet. The burning of fossil fuels is the primary source of carbon dioxide emissions, which are the largest contributor to human-caused climate change. 

• Carbon dioxide levels in the atmosphere have increased by over 40% since preindustrial times, from 280 parts per million (ppm) to over 415 ppm in 2021 (NASA). 

• The annual average concentration of carbon dioxide in the atmosphere reached a record high of 412.5 ppm in 2020 (World Meteorological Organization). In 2019, the burning of fossil fuels accounted for 89% of global carbon dioxide emissions from human activities (Global Carbon Project).

• Transportation is a significant contributor to greenhouse gas emissions, as cars, trucks, and planes all burn fossil fuels. 

• Transportation is responsible for approximately 23% of global energy-related greenhouse gas emissions (International Energy Agency).

• The production of electricity also contributes to greenhouse gas emissions, as many power plants still rely on fossil fuels to generate electricity. 

• Coal-fired power plants are the largest source of greenhouse gas emissions from electricity generation (Environmental Protection Agency).

Deforestation

Deforestation is the clearing of forests for agriculture, urbanisation, or other uses. 

• Deforestation contributes to climate change by reducing the amount of carbon that forests can absorb from the atmosphere. 

• Trees absorb carbon dioxide from the atmosphere and store it in their biomass. When forests are cleared, the carbon stored in the trees is released into the atmosphere, contributing to the increase in greenhouse gases. 

• Deforestation also reduces the number of trees that are available to absorb carbon dioxide from the atmosphere. This can create a positive feedback loop, where deforestation leads to more carbon dioxide in the atmosphere, which leads to more warming, which can cause more deforestation.

• Between 1990 and 2016, the world lost 1.3 million square kilometers of forest, an area larger than South Africa (Global Forest Watch). 

• Deforestation rates in tropical countries have increased by 43% since the 1990s (Global Forest Watch).

• The rate of deforestation in the Amazon rainforest increased by 30% between 2018 and 2019 (National Institute for Space Research, Brazil). The loss of forests in the Amazon, due to deforestation and wildfires, resulted in the release of an estimated 2.2 billion metric tons of carbon dioxide in 2019 (Carbon Brief). 

• Deforestation is responsible for the loss of approximately 10 billion trees each year (World Wildlife Fund).

Deforestation accounts for approximately 10% of global greenhouse gas emissions. (World Resources Institute)

Industrial Activities

Industrial activities such as manufacturing, mining, and transportation release greenhouse gases into the atmosphere. 

• Industrial activities often require the burning of fossil fuels, which is a major contributor to climate change. 

• Industrial activities also produce other pollutants that can contribute to climate change, such as black carbon, which absorbs sunlight and contributes to warming.

• Industry accounts for approximately 22% of global greenhouse gas emissions. (Environmental Protection Agency)

• The cement industry is responsible for approximately 8% of global carbon dioxide emissions. 

• The steel and iron industry is responsible for approximately 7% of global carbon dioxide emission. 

• The chemical and petrochemical industry is responsible for approximately 3% of global greenhouse gas emissions (International Energy Agency).

Methane emissions from oil and gas production have increased by more than 30% since 2000 (Environmental Defense Fund).

Agriculture and Livestock Production

Agriculture and livestock production also contribute to climate change. The clearing of land for agriculture also contributes to deforestation, further exacerbating the problem.

• Agriculture is responsible for approximately 24% of global greenhouse gas emissions when indirect emissions from land use changes are included (Food and Agriculture Organization of the United Nations). 

• Rice cultivation is responsible for approximately 10% of global methane emissions (Environmental Defense Fund).

• The use of nitrogen fertilisers in agriculture is responsible for approximately 10% of global nitrous oxide emissions (Environmental Protection Agency). 

Livestock also produce methane, a potent greenhouse gas that is 28 times more effective at trapping heat than carbon dioxide. 

• The production of meat and dairy products requires large amounts of energy, which often comes from the burning of fossil fuels. 

• The production of beef is responsible for approximately 41% of agriculture-related greenhouse gas emissions (Food and Agriculture Organization of the United Nations).

• Livestock production accounts for approximately 14.5% of global greenhouse gas emissions, primarily from methane emissions from enteric fermentation and manure management (Food and Agriculture Organization of the United Nations).

Land Use Changes

Land use changes, such as urbanisation and the conversion of grasslands and wetlands into croplands, also contribute to climate change. These changes can result in the loss of carbon storage capacity in soil and vegetation, which can lead to the release of stored carbon into the atmosphere. Land use changes are responsible for the loss of approximately 12 million hectares of forest each year (World Wildlife Fund). 

• Urbanisation is responsible for the loss of approximately 1.5 million hectares of land each year (United Nations).

• The conversion of forests into agricultural land is responsible for the majority of land use-related greenhouse gas emissions. 

• The conversion of grasslands and wetlands into croplands and pasturelands is responsible for approximately 12% of global greenhouse gas emissions (World Resources Institute).

• Peatland degradation and drainage is responsible for approximately 5% of global greenhouse gas emissions (Global Peatlands Initiative). 

Waste Generation

The generation and disposal of waste also contribute to climate change. Landfills are a significant source of methane emissions, which are a potent greenhouse gas. The production and transportation of goods and packaging materials also contribute to greenhouse gas emissions.

• In 2016, the world generated 2.01 billion metric tons of municipal solid waste with a per capita average of 0.74 kg per day with significant variations between countries and regions (World Bank).

• Landfills are responsible for approximately 15% of global methane emissions. (Environmental Protection Agency)

• The production of plastic is responsible for approximately 8% of global oil consumption, and this is projected to double by 2050 (Ellen MacArthur Foundation). 

• The improper disposal of plastic waste is responsible for significant environmental damage, including the release of greenhouse gases and pollution of land and water resources (World Wildlife Fund).

• The organic fraction of municipal solid waste is responsible for approximately 5% of global methane emissions (Environmental Protection Agency).

See also:

https://royalsociety.org/topics-policy/projects/climate-change-evidence-causes/

A composite index is a statistical tool used to combine multiple indicators or variables into a single score or ranking. Composite indexes are used in many fields, including economics, social sciences, environmental studies, and healthcare, to measure complex concepts that cannot be captured by a single indicator or variable.

Composite indexes are created by selecting a set of indicators or variables that are relevant to the concept being measured, such as economic development, social well-being, or environmental sustainability.

The indicators are typically measured using different units or scales, which must be normalised to ensure comparability. The indicators are then weighted to reflect their relative importance in the composite index, and aggregated using a mathematical formula to create a single score or ranking.

Composite indexes can provide a more comprehensive and nuanced understanding of complex phenomena than individual indicators or variables alone.

For example, a composite index of economic development might include indicators such as gross domestic product (GDP), income inequality, and employment rate, which can provide a more comprehensive picture of economic well-being than GDP alone.

Composite indexes are used for a variety of purposes, including policy-making, resource allocation, and benchmarking. For example, governments may use composite indexes to identify areas of the country that require additional resources or to evaluate the effectiveness of policies aimed at improving social or economic outcomes.

Steps of Making Composite Index

Here are the steps of Preparing Composite Index:

Indicator selection: The first step is to select a set of indicators that capture the characteristics of the regions being studied. This involves identifying the key dimensions of regional development or performance, such as economic growth, social inclusion, environmental sustainability, or infrastructure quality. The selection of indicators should be based on a clear conceptual framework that specifies the linkages between the indicators and the research question at hand. The indicators should also be relevant, reliable, and available for all regions of interest.

For example, if we wanted to create a composite index to delineate regions in a country based on their economic development, we might select indicators such as per capita income, employment rate, poverty rate, and GDP growth rate.

Data collection: Once the indicators have been selected, data needs to be collected for each indicator from reliable sources. This may involve accessing existing datasets, such as national statistical databases or regional surveys, or collecting new data through surveys, fieldwork, or other means. It is important to ensure that the data is comparable across regions and over time, and that any missing or incomplete data is properly addressed.

For example, we might access existing datasets such as national statistics or conduct new surveys to collect data on employment rates.

Normalisation: Since the indicators may have different units of measurement and ranges of values, they need to be normalised to a common scale to ensure comparability. This involves transforming the raw data into a standardised form that reflects the relative performance of each region on each indicator. The most common methods of normalisation include standardisation, min-max scaling, or z-scores. These methods adjust the values of each indicator so that they have a mean of zero and a standard deviation of one, a range of 0 to 1, or a score relative to the mean and standard deviation of the whole dataset, respectively.

Weighting: After normalisation, the indicators need to be weighted to reflect their relative importance in the composite index. This involves assigning a weight to each indicator that reflects its contribution to the overall performance of the regions.

The weighting can be done using subjective or objective methods, such as expert opinion, stakeholder consultation, or statistical analysis. The most common methods of objective weighting include principal component analysis, factor analysis, or regression analysis. These methods identify the underlying factors or dimensions of the data and assign weights based on their explanatory power or correlation with the research question.

For example, we might use expert opinion or statistical analysis to determine that per capita income is more important than GDP growth rate, and assign a weight of 0.4 to per capita income and a weight of 0.2 to GDP growth rate.

Aggregation: Once the indicators have been normalised and weighted, they can be aggregated into a composite index using a mathematical formula, such as a weighted sum or a geometric mean. This involves combining the values of each indicator for each region into a single score or ranking that reflects the overall performance of the region. The formula should be consistent with the conceptual framework and the weighting scheme, and should take into account any trade-offs or complementarities among the indicators. The resulting composite index provides a useful summary measure of regional development or performance that can be used for benchmarking, monitoring, or policy-making purposes.

For example, we might use a weighted sum formula to combine the normalised indicators into a single score for each region, where the score for each region is the sum of the weighted normalised values for each indicator.

Validation: Finally, the composite index needs to be validated to ensure its reliability and validity. This involves testing the internal consistency, stability over time, and correlation with external measures of the regions being studied.

Internal consistency refers to the degree of correlation among the indicators and the composite index, and can be tested using statistical methods such as Cronbach's alpha or factor analysis.

Stability over time refers to the degree of change in the composite index across different time periods, and can be tested using statistical methods such as correlation analysis or regression analysis.

Correlation with external measures refers to the degree of association between the composite index and other measures of regional development or performance, such as economic growth, poverty rates, or environmental quality, and can be tested using statistical methods such as correlation analysis or regression analysis. Validation helps to ensure that the composite index is a robust and accurate tool for delineating regions and informing policy-making decisions.