Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.

Evolution is the theory that plants and animals today have come from kinds that have existed in the past. Scientists such as Charles Darwin and Alfred Wallace dedicate their life to observe how species interact with their environment, grow, and change. We are able to predict future changes as well as simulate the process using genetic algorithms. Genetic Algorithms give us the opportunity to present multiple variables and parameters to an environment and change values to simulate different situations. By optimizing genetic algorithms to hold entities in an environment, we are able to assign varying characteristics such as speed, size, and cloning probability, to the entities to simulate real natural selection and evolution in a shorter period of time. Learning about how species grow and evolve allows us to find ways to improve technology, help animals going extinct to survive, and figure* out how diseases spread and possible ways of making an environment uninhabitable for them. Using data from an environment including genetic algorithms and parameters of speed, size, and cloning percentage, the ability to test several changes in the environment and observe how the species interacts within it appears. After testing different environments with a varied amount of food while keeping the number of starting population at 10 entities, it was found that an environment with a scarce amount of food was not sustainable for small and slow entities. All environments displayed an increase in speed, but the environments that were richer in food allowed for the entities to live for the entire duration of 50 generations, as well as allowed the population to grow significantly.

I am spending my last weeks on thinking about climate actions, sustainability and economical aspects of these. I listened to a lot of podcasts about these topics, and I have tens of notes from these podcasts. I decided to express my ideas about this topic. But no, it is not my turn to spread the ideas. Only thing I wrote for this article is headline and a couple of keywords.

Traditionally, policymakers and urban planners haven't had access to city data that can reveal complex patterns and relationships between factors that influence urban development. In some cases, data is too laborious or costly to measure at frequent time intervals, and in others, unexpected or unforeseen circumstances such as a pandemic like COVID-19 are responsible for invalidating earlier forecasts. But this is changing rapidly, with emerging technologies unlocking new possibilities for urban planning. Advances in emerging technologies like Artificial Intelligence and Machine Learning can help us understand our cities better and derive useful insights from real-time data collected through automated models that provide a much closer view of the situation on-ground compared to traditional approaches. These insights can properly assess public interests and help policymakers in making decisions that are more sustainable.

Africa is home to the world's fastest growing population, which is expected to double by 2050. This growth is directly linked to the increase in demand for energy – indeed the African Energy Chamber projects that the continent's demand for power will keep rising between 4-5% per year, possibly doubling by 2050. A reversal of fortune for the world's unelectrified population is one of the Sustainable Development Goals of the United Nations (SDG7). African governments have traditionally relied on centralised grid expansion to improve electricity access. This requires significant capital expenditure and is often not time or cost effective, especially in rural areas where much of Africa's unelectrified population live. At the same time, the Paris Agreement enshrines the global aim to achieve Net Zero in the next 3 decades in order to meet the goal of keeping global temperature rise well below 2 degrees Celsius above pre-industrial levels.

Corn, coffee, chocolate, even wine are a few of the foods that stand to be massively disrupted by the effects of climate change, population growth and water scarcity -- if they haven't already. A recent study found the yields of the world's top ten crops have begun to decrease, a drop that is disproportionately affecting food-insecure countries. The situation stands to worsen. Researchers project that the global population will increase by 3 billion in 2050. To feed these additional global residents, agricultural production must increase by 50 percent, says Dr. Ranga Raju Vatsavai, an associate professor in computer science at North Carolina State University and the associate director of the Center for Geospatial Analytics.

Predicting where Ebola might strike next could become easier, thanks to a new computer model. The model tracks how changes in the environment and in human societies could affect the deadly virus's spread. It predicts that Ebola outbreaks could become as much as 60 percent more likely by 2070 if the world continues on a path toward a warmer climate and a cooling economy. Ebola, on average, kills half of all people who contract the virus. In previous outbreaks, the fatality rate has risen to as high as 90 percent.

For those who are new to AI, Machine Learning, and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed, such as the need for improved sharing of data by healthcare participants including hospitals and ensuring the highest quality in the preparation of data, are entirely valid and I take the view that the need for access to data and sharing of data by hospitals may need to become a matter of political and regulatory concern.

In countries around the world, natural disasters have been much in the news. If you had a hunch such calamities were increasing, you're right. In 2017, hurricanes, earthquakes, and wildfires cost $306 billion worldwide, nearly double 2016's losses of $188 billion. Natural disasters caused by climate change, extreme weather, and aging and poorly designed infrastructure, among other risks, represent a significant risk to human life and communities. Globally, $94 trillion in new investment is needed to keep pace with population growth, with a large portion of that going toward repair of the built environment.

Important resources like minerals, oil and diamonds often go hand-in-hand with conflict and poor governance. But when it comes to one particular resource -- the most important resource of all -- many think a different theory will hold true. Often referred to as the water wars thesis, it suggests that growing water scarcity will drive violent conflict as access to water dries up for certain communities. Analysts worry that people, opportunistic politicians and powerful corporations will battle for dwindling water supply, inflaming tensions. In a new study, researchers tried to map out how water wars will emerge around the world and which countries are most likely to see water-related conflict in the coming decades.