Machine Learning for Ecological Sustainability: An Overview of Carbon Footprint Mitigation Strategies

Introduction

Human existence is inextricably intertwined with nature. Ecology by its inherent trait supplies humanity with vast natural resources. It aids in the sustenance of the huge living population and compensates for the ecological imbalances that surface repeatedly. Due to the rising global population, the consumption of nature’s wealth is ever-increasing. The surge in production of energy, food, goods, services, etc., to meet the demand and supply gaps has led to a colossal depletion of raw materials worldwide over the years. Overexploitation of the flora and fauna has endangered numerous species while putting several others in the high-risk category threatening the ecosystem’s equilibrium.

A sustainability metric for measuring human impact on Earth's ecosystems called the “Ecological Footprint” was proposed in the early 1990s by two Ph.D. researchers at the University of British Columbia (Wackernagel & Rees, 1996). Considering the dependency of humanity on the biosphere, Ecological Footprint (EF) is defined as a measure of the area necessary to sustain any given population. In its broadest sense, it is a measure that incorporates all forms of water and energy use, infrastructure, forest management, and other material inputs required by humans to flourish day in and day out, as well as accounting for the land devoted to waste assimilation. Ecological Footprint per capita is one of the most widely recognized indicators of environmental sustainability. Human society becomes unsustainable when its Ecological Footprint surpasses its biocapacity. Considering the sustainability of natural resources becomes essential due to the burgeoning demands of the growing population. Ecological Footprint and sustainability are emerging research areas that have grabbed the attention of contemporary researchers and policymakers.

Figure 1.

Components of Ecological Footprint

The carbon component of the Ecological Footprint, highlighted in red in Figure 1, is also an indication of the amount of forest land that will be required to absorb the Greenhouse Gas (GHG) emissions from the burning of fossil fuels. The carbon footprint measures the amount of greenhouse gas released due to the consumption of fossil fuels excluding the fraction absorbed by the oceans. The amount of greenhouse gas emitted into the atmosphere when fossil fuels are burned contributes directly to an ecological footprint. As more greenhouse gases are released into the atmosphere, there will be a need for more sea and forest areas to remove them. Lacking the requisite sea and forest areas will increase the carbon footprint. A larger carbon footprint implies a more substantial ecological footprint. In this chapter, we examine the use of Machine Learning to address the problem of increasing carbon footprint in the context of ecological footprints and sustainability.

Machine Learning (ML) is a progressive technology that has the potential to offer practical solutions for environmental sustainability. Machine Learning has much to offer in terms of monitoring, analyzing, and resolving sustainability issues. Even with exceptional advancements in the field, the area continues to have a lot of scope for improvement. The discipline’s ever-expanding horizons still hold plenty of opportunities for solving challenging real-world problems. Artificial Intelligence (AI) and ML handle complex data, enabling data scientists to make accurate forecasts. These technologies can recommend comprehensive rational solutions that help in achieving sustainable development across the globe.

Prior research on Machine Learning applications in the sustainability domain is promising, and we believe that Machine Learning can support the development of culturally tailored organizational processes and individual responsibilities to cut down natural resources and energy consumption. We believe that ecological sustainability is the key to balancing the rising Ecological Footprint. Eventually, AI/ML will be highly valuable in contributing to environmental governance and not just limited to minimizing society's energy, water, and land usage intensities. In this survey, we study the applications of Machine Learning to analyze and predict the impact of the Ecological Footprint and to strategize carbon footprint reduction.

Methodology

We started by framing the following research questions: