The Role of Data Analytics and Artificial Intelligence in Addressing Climate Change

One such source contributing to the understanding of climate technology and analytics is The Economist’s series of articles related to artificial intelligence and climate change. In a series of articles, The Economist identifies how analytics and AI can make a difference in dealing with climate issues, such as using them for increased efficiency, forecasting, and decision-making in a system involving a large degree of complexity. Such an approach views analytics not as a technology with the capacity to resolve climate change but rather an enabler for dealing with climate change.

Economist magazine highlights how climate change is a data challenge in essence. Climate Earth comprises a massive amount of information, such as weather systems, usage of energy, agricultural production, emission statistics, and inf rastructure performance, which is very hard to make sense of without using analytics. Through using climate modeling using AI, predictive analysis, and optimization solutions, Economist magazine tells how analytical solutions can aid in better risk and uncertainty management. For instance, better climate models enable policymakers to predict heavy weather with higher accuracy, thus allowing early warning systems and better disaster readiness.

Additionally, the source links analytics with mitigation of climate change based on efficiency gains. The Economist regularly highlights how AI-powered analytics can smoothen electricity consumption in power grids by increasing efficiency in energy usage, in addition to optimizing logistics in supply chains. Such examples prove that by making decisions based on analysis of data, emissions can be reduced without necessarily relying on major behavioral shifts. Therefore, analysis can be approached for making climate mitigation more feasible.

Worth noting, too, is that The Economist does not come across as if climate analytics is a risk-free or foolproof technology. The magazine recognizes that climate analytics using AI is a very resource-intensive process in terms of computation and energy, and this can be a source of greenhouse gas emissions if these energy sources are fossil fuels. Of course, this lends a degree of credibility to this stance because it recognizes that climate analytics need to be used in a responsible manner.

Instead, by emphasizing practical application over pure innovation, The Economist informs public perception of climate technology being application-centric and focused on delivering results. Analytics are presented in the magazine as a tool for making decisions in a way that enables better resource allocation and a better understanding of climate risk, which in turn affects how policymakers, investors, and students view climate technology not in terms of experimental research but rather a necessity. In summary, The Economist series relates climate change and analytics in a manner which highlights how analytics can increase understanding, efficiency, and resilience in a world under climatic uncertainty. Although this series of articles does not assert a solution for climate change through analytics, it definitely promotes the growing need for making smarter decisions with climate change using data.

Reference: The Economist. (2023). How artificial intelligence could help to fight climate change. https://www.economist.com

"Robert Keepers: Why JPMorgan Hired a Head of Climate Tech"

 

One of the world’s largest financial institutions is creating a new position in its leadership to better invest in climate technologies and to accelerate the growth of clean energy and climate analytics. In addition, JPMorgan has already deployed $900 billion toward its $2.5 trillion sustainable financing commitment. In my climate technology course, we study climate impacts through data, mapping, and geospatial analysis. But this article adds a valuable dimension to the climate‑technology conversation by highlighting the growing role of major global financial institutions in climate‑tech investment and demonstrating just how significant this industry is becoming. It underscores how influential private‑capital firms are in determining the trajectory of the climate‑tech sector and its potential to become a core industry. It indicates that major financial institutions are accelerating their engagement with the climate‑tech industry, signaling a broader shift in investment priorities. 

This article highlights the major shift in the major financial institutions in how they are valuing and taking the climate technology industry as an important sector. This is signaling to the financial investment sector that the climate technology industry is a future core sector for economic growth. This reflects a notable shift in thinking, as the climate‑technology industry has historically been perceived as a risky space with no guaranteed returns, which has limited investor interest. It further reflects a shift toward valuing positive climate impact, pushing for other major corporations to evaluate and reduce the environmental effects of their activities. JPMorgan's creation of a dedicated position for the Head of Climate Tech is a message to the industry of its commitment to invest in the climate technology sector. It’s a strong stance that commitment is a long-term implementation rather than after thought to the industry. It recognizes the climate technology industry as a fast-moving investment opportunity along with its climate-beneficial impact. It demonstrates that when major financial institutions decide to invest and support the new climate technologies, it often accelerates the expansion of the sector. 

This article highlights the private capital aspect of climate tech being an investment opportunity. But it overshadows the climate technology core value of creating solutions to help with the climate crisis the world is facing. It raises questions about whether the climate‑tech industry will be driven by profit margins in the future in ways that may sideline need‑based, innovative solutions. It does not address how this shift in position will assess the risks associated with climate technologies or evaluate the real‑world impact of such a long‑term commitment. It further highlights that all eight major banks have withdrawn from the Net‑Zero Banking Alliance, effectively abandoning their 2030 net‑zero goals, which raises broader questions about the credibility and durability of voluntary climate‑finance frameworks. A major financial institution such as JPMorgan plays a pivotal role in shaping the economic trajectory of the climate‑technology sector. The establishment of a dedicated climate‑tech division signals to the broader market that this industry represents a strategically significant and financially viable investment. Such a move not only legitimizes the sector but also increases the likelihood that other major financial institutions will follow suit, amplify capital flows, and accelerate industry‑wide growth.

Reference: 

Robert Keepers: Why JPMorgan Hired a Head of Climate Tech | Sustainability Magazine

Understanding Zillow’s Climate-Risk Score Removal: Market Pressures vs. Consumer Transparency

Beginning in September 2024, Zillow displayed climate-risk scores generated by First Street, a New York–based startup that analyzes property-level exposure to hazards such as floods, fires, wind, air quality, and heat. Using historical and publicly available data, First Street aimed to give homebuyers a clearer understanding of the environmental risks tied to each property.

However, just over a year after launching the feature, Zillow removed the scores from more than one million listings. The change followed significant pushback from the California Regional Multiple Listing Service (CRMLS) and real estate agents across several states. Some agents argued that the risk labels introduced concerns that buyers wouldn’t otherwise have had, potentially harming sales. Although Zillow no longer shows the scores directly, it now provides a link directing users to First Street’s external site, ultimately making climate-risk information less prominent and less transparent than before.

The removal of Zillow’s climate-risk scores genuinely surprised me because they offered a level of transparency that homebuyers rarely had access to. When exploring First Street’s website, it’s clear their analysis goes far beyond a simple 1–10 rating. They provide risk maps, histories of past weather-related events, projected insurance cost changes, and other meaningful data that help buyers understand long-term exposure. It’s reassuring that the information still exists, but disappointing that it has been reduced to a link that many buyers may overlook.

I strongly believe Zillow should prioritize transparency for buyers over real-estate agents’ sales concerns. As Mathew Eby pointed out, “The risk doesn’t go away; it just moves from a pre-purchase decision into a post-purchase liability.” That statement captures the core issue. Investors, insurers, and cities already use climate-risk analytics to make decisions, and insurance companies often raise premiums or withdraw coverage entirely when properties become too risky. We’ve already seen lawsuits from Los Angeles homeowners whose wildfire-related claims were denied after the Eaton and Palisade fires. While some question the accuracy of climate-risk models, First Street’s analysis identified over 90% of the homes that eventually burned — suggesting the models are more reliable than critics assume.

Ultimately, this feels like a battle between real-estate agents and the average homebuyer. As the article notes, “In offering homebuyers’ access to the same data, Zillow helped level the playing field. But thanks to the objections of real estate agents, consumers have one more hoop to jump through.”, and it’s a hoop they shouldn’t have to deal with. In an era where climate risks are increasing, access to clear information is essential for making responsible financial decisions. For that reason, Zillow should reinstate First Street’s climate-risk data directly on listings rather than hiding it behind a link.

References: 

912 LIGHTHOUSE DR, NORTH PALM BEACH, FL 33408 | Climate Risk Report | First Street

LA homeowners are suing insurance companies for not covering damages from the fires : NPR

Zillow drops climate risk scores after agents complained of lost sales | TechCrunch

"They Held a Climate Summit in the Amazon. They Didn’t Account for the Rain."

The article that I chose to read for the 3^rd blog was from The Wall Street Journal, titled “They Held a Climate Summit in the Amazon. They Didn’t Account for the Rain.” In this article, that talk about the summit, COP30, held in Belém, Pará, Brazil. The chosen area for the summit is significant in this article because this is the gateway city to the Amazon rainforest. While the delegates were there to negotiate climate action, they experienced a taste of what vulnerable regions are currently experiencing with storms and heavy rainfall. The article describes that they had many issues, such as tents leaking, roof holes, water seeping into ventilation, and not being able to hear themselves over the rain. One of the bigger impacts from the weather was a fire breaking out as well. Overall, the article highlights that this summit was one big disaster for all of the attendees. As states in the article, “The high temperatures do help frame conversation on the reality of what we’re dealing with.”

              I thought this article was a perfect example of what we’re researching in this class. As the article highlights, we’re starting to see how the climate is starting to get much worse, especially in places like the Amazon rainforest where it’s becoming more extreme and unsafe to live in. I brought this article up in particular because it’s a perfect example of how people aren’t taking climate change into account yet. For setting up a summit in near the Amazon rainforest, you’d think they would take the weather into consideration when planning to put people in tents. However, for those who aren’t living in these extreme weather situations, they aren't prepared for the harsh reality when it comes for them. Another example I have for this is what happened to Long Beach Island in New Jersey, which is what I’m doing for my final project. When Hurricane Sandy hit the island, a lot of home owners weren’t prepared for the flooding or harsh weather. The storm ended up wiping out a lot of the houses on the island that weren’t above ground. Now, years later, you can see that most of the houses on the island are above ground, especially near the shore, because they don’t want the same thing to happen again.

              Overall, I thought this article gave some perfect insight of how people are currently reacting to the climate crisis. In the future, I hope we are more advanced with predicting our weather so accidents like this one don’t happen again and so we can help keep people safe in cases of extreme weather.

 

Article: https://www.wsj.com/us-news/climate-environment/they-held-a-climate-summit-in-the-amazon-they-didnt-account-for-the-rain-19373680?mod=environment_news_article_pos4

MIT Explained: Generative AI's Environmental Impact

After reading the MIT News article on the environmental impact of generative AI, I found myself thinking a lot about the hidden side of “the cloud.” I usually picture AI as this clean, digital thing, type a question, get an answer, but the article is a reminder that there’s a very real physical cost behind it. Massive data centers, huge energy demand, tons of water for cooling, all of that is part of the story every time generative AI is used. And I completely agree with the author on this point, AI isn’t magical. It runs on electricity, and right now, that energy usually comes with a carbon footprint attached.

I also appreciated the way the article pushed back against the idea that only training these big models is the problem. The environmental impact continues every single time someone runs a prompt. Inference, the part we interact with, is happening constantly and everywhere. It’s easy to forget that because the burden is invisible to the user. I think the article makes that point really well, and it’s something most people probably don’t realize.

That said, there were a few things I wish the article had gone deeper on. For example, the piece focuses heavily on the costs of generative AI, but it barely touches on the benefits. AI isn’t just about writing essays, it’s also being used to optimize supply chains and model climate scenarios, and improve efficiency in ways that could reduce emissions. I’m not saying that cancels out the environmental footprint, but I do think a fair discussion needs to look at the full picture, not just the downside.

Another point where I felt the article oversimplified things is around energy sources. Yes, data centers use a lot of power. But not all data centers are equal. Some run on mostly renewable energy, some use advanced cooling systems, and the efficiency of AI hardware is improving really quickly. The article acknowledges this a little, but it mostly sticks to a “things are getting worse” narrative. In reality, there’s a lot of innovation happening right now.

I also wish the article had talked more about accountability. Who should be responsible for managing AI’s environmental footprint? The companies building these models? Governments? Users? Without clear reporting standards for energy use, water consumption, and emissions, it’s almost impossible to know whether we’re actually making progress. Transparency is a huge missing piece in this conversation.

But overall, the article made me think, in a good way. It highlights a problem that’s easy to ignore because it feels distant and abstract. It’s a reminder that digital tools still have real-world consequences. At the same time, I’m hopeful. If we push for better standards, better reporting, and better technology, AI doesn’t have to be at odds with environmental goals. It can actually help us reach them. The challenge is making sure we’re honest about the trade-offs, and willing to design systems that don’t hide the cost.

Reference: Explained: Generative AI’s Environmental Impact,  https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117

Analytics and Climate Change: A Critical Review of “Data to Decisions”

In his article “Data to Decisions: How Technology Can Solve a $1.2 Trillion Climate Change Problem” (World Economic Forum, February 2, 2024), Himanshu Gupta argues that analytics and climate data present a significant chance for businesses to manage risk and create value in a warming world. Referring to a CDP Global Supply Chain Report, he warns that suppliers could face around $1.26 trillion in potential revenue losses over five years. He suggests that data tools, including remote sensing, IoT sensors, AI, and machine learning climate models, can transform uncertainty into actionable decisions. By framing climate analytics as a strategic advantage instead of just an environmental duty, Gupta effectively grabs the attention of businesses.

The article's main strength is how it connects new technologies with real business uses. Gupta shows how companies can use analytics for supply chain planning, asset risk assessment, and production choices. For example, he points out an agribusiness using AI based weather simulations to change planting windows and a construction materials company that positions production facilities in anticipation of hurricanes. These examples make “climate data” concrete, bridging the gap between environmental science and business strategy.
However, the article has several weaknesses. Most importantly, the claim that climate data could unlock $1.2 trillion lacks clarity. Gupta offers no clear method or assumptions to back up this figure no breakdown by sector, time frames, or geographic focus. Without that context, the number seems more rhetorical than analytical. A convincing economic argument should at least briefly explain how that value was estimated or reference supporting data.
Another issue is Gupta’s optimism about technology. The article suggests that digital tools alone can drive change but pays little attention to real world challenges, such as high implementation costs, poor data governance, limited interoperability, and a lack of analytical skills. Many small businesses and public institutions cannot afford complex models or sensor networks. Overlooking these challenges risks oversimplifying the issue. A more balanced discussion would recognize the need for policy incentives, partnerships, and skill-building to make analytics available beyond large corporations.
Gupta also downplays uncertainty in climate models. Climate projections rely on assumptions and data that often struggle to account for unprecedented or extreme events. Treating analytic results as accurate forecasts can lead to misunderstandings. The article could have improved its credibility by emphasizing solid decision framework, those that explore multiple scenarios and prepare for uncertainty rather than suggesting data alone can “solve” climate risk.
Equity is another aspect that is missing. The companies best equipped to leverage climate analytics are usually wealthy multinationals. Smaller businesses, suppliers in developing regions, and vulnerable communities often lack access to these tools. Without fair data sharing and capacity building, the advantages of climate analytics may widen existing inequalities. Gupta's vision would be more convincing if it discussed how to democratize data access or help developing economies adopt these technologies.
Finally, while Gupta advocates for collaboration, he offers little guidance on the institutional or policy structures required to expand these efforts. Questions remain about how to incentivize firms to share data, what standards should regulate its use, and how regulators can guarantee data quality and fairness. Without addressing these issues, the article feels more aspirational than practical.
In conclusion, Gupta’s article makes a strong case for using analytics to address climate challenges. It effectively repositions climate data as both a risk management tool and a growth opportunity. However, the argument would be stronger with clearer methodology, a focus on barriers to adoption, recognition of model uncertainty, consideration of equity, and specific policy suggestions. Without these elements, the promised $1.2 trillion in value remains an inspiring vision but not a fully credible roadmap for change.

Source: Gupta, Himanshu. “Data to Decisions: How Technology Can Solve a $1.2 Trillion Climate Change Problem.” World Economic Forum, 2 Feb 2024. https://www.weforum.org/stories/2024/02/data-decisions-technology-climate-change-problem/

Robots Beneath the Waves: Monitoring the Ocean’s Changing Carbon Cycle

Miles beneath the ocean’s surface, hundreds of free-floating robots are quietly measuring how the Earth breathes and how marine heatwaves are altering that rhythm. The Global Ocean Biogeochemical (GO-BGC) Array, led by the Monterey Bay Aquarium Research Institute (MBARI), has deployed a global fleet of autonomous robots to better understand the ocean’s changing environment.

Traditional methods of studying the ocean’s biogeochemical structure using satellites, buoys, and ships are limited in depth and coverage. These new robotic floats fill that gap by continuously monitoring the ocean’s biological, chemical, and physical properties. Each float drifts at about 1,000 meters below the ocean’s surface for nine days, then descends to 2,000 meters before returning to the surface to relay data via satellite. This cycle repeats endlessly, collecting vital information on oxygen, pH, nitrate, suspended particles, chlorophyll, temperature, conductivity, and depth.

10-day BGC-Argo robot cycle for collecting and transmitting data

As MBARI senior scientist Ken Johnson explains, “Marine heatwaves cause changes in ecosystem structure—in the plankton and how they operate—and these shifts in carbon export and how the ocean sequesters carbon are changing the services the ocean provides to us.”. This revelation raises important questions and insights through the intersection of technology, climate change, and the ocean’s health.

COP30 Kicks Off: How Climate Technology Can Make a Difference

 

As COP30 begins in Belém, Brazil, the IO+ article, "How Climate Technology Can Make a Difference" offers a hopeful yet realistic view of the role innovation can play in addressing the climate crisis. The authors spotlight emerging technologies from the Netherlands, such as Paebbl's CO2 mineralization, Carbyon's direct-air-capture machines, and SeaO2's seawater carbon removal, as examples of how science and technology can advance the Paris agreement goals. While I agree that such innovation is a positive thing, I think the article leaves out the issue of technology not being able to succeed without human and more specifically political agreements.

"Climate resilience technology: An inflection point for new investment"

McKinsey & Company analyzes the climate resilience technology market and expects the growth market to be between 600 billion and 1 trillion by 2030, reflected by the rising demand for technology that can counter the effects of climate disasters. Understanding the importance of the market in the face of a growing climate, they outline the market need for significant action and a variety of investors. Now, with this understanding, McKinsey built a framework to help investors consider opportunities in this field. The authors focused on the framework to be about the need for adaptation in the climate-resilient technology market. The framework identifies ten technology categories as attractive investment opportunities, then creates subsets within these categories to refine the criteria and estimate the market size of each subset. This article directly ties to the theme of my Climate Technology course, where we use geospatial data and weather APIs to analyze real-world climate events. 

The key strength of the article is the identification of analytics and technology being the focus on building climate resilience. Developing the framework, the authors recognize their sole focus on private capital is “... only a part of what’s needed for full adaptation …” (McKinsey 2025). While the projection of $1 trillion growth in the market highlights the financial importance of climate resilience technology, it also reveals how the authors are treating resilience technology as an investment market. With the sole focus on private capital, it risks sidelining public and community-led adaptation efforts. Climate resilience requires the contribution of everyone's action and not just the focus on the market. Building climate resilience is a shared challenge that depends on public data and collective decision-making. For instance, the coast of Maryland is developing a project to use wind power mill technology to promote sustainable energy solutions for the growing energy demand. This project of this scale is not driven by profit incentive or private capital, but it relies on the federal and state government, public data sharing, and community advocates. The core of this project lies in the collective contribution of everyone, demonstrating that climate resilience cannot simply emerge from market forces. McKinsey's sole emphasis on the private capital investor skips over the other major roles that play into fully adapting climate resilience technology. 

The author’s analysis uses more than 200 adaptation technologies that were narrowed down to 49 priority technologies, illustrated in the figure. The chart shows that the largest investment opportunities are in resilient buildings, while the lower categories are projected to receive less funding. The narrowing of the technologies raises questions about what the criteria were used to define as a priority. If the criteria were defined based on the best financial return, then the process would remove other important community or nature-based solutions. This process could introduce a subtle bias of ignoring community-level adaptation and treating it as only an investment portfolio. Having a balanced approach to the criteria could ensure it serves the community and the market. Although the McKinsey article strongly conveys the urgency of climate resilience investment, it does not provide much of the technical detail of how the data analytics are performed. It does not explain or provide any details of how the framework is trained and the data collection process to ensure their analysis is accurate. In the absence of this information, the process cannot be considered transparent. Analytics can provide valuable insights into climate resilience technology, but it can risk only serving market interests. A balanced approach is needed to incorporate everyone’s actions in fully adapting to climate resilience.

Reference

Climate resilience technology: An inflection point for new investment

"It’s Getting Harder to Figure Out Whether You Live in a Flood Zone or Not"

 

The article I chose to go with for my second blog is “It’s Getting Harder to Figure Out Whether You Live in a Flood Zone or Not.” from the Wall Street Journal. I thought this article relates perfectly to our class because it goes over the importance of having up to date weather data for forecasting future flood zones.

 

One of the main points of the article is trying to make is that outdated federal flood maps are leaving millions of U.S. homeowners unaware of their true flood risk. While FEMA’s maps list about 8 million properties as high-risk, the article includes a private model that estimates “another nearly 13 million properties outside the zones with the same level of flood risk.” Another thing they point out, which doesn’t help with forecasting flooding, is that the government’s maps are often 10 or more years old and rely on historical data.” In the article they detail this as significant because these outdated maps fail to reflect changing realities like heavier rainfall and urban drainage failure. An example they use in the article is from a Chicago homeowner who states, “’That is what they say: We are in a no-flood zone’” despite having repeated basement floods. Like we’ve discussed in class, and as they mention in the article, this makes it harder for homeowners to be insured when flooding happens, especially when they’re not considered to be in a flood zone. The article further points out that attempts to update or expand flood zones face resistance and that, according to a federal advisory council, “it can take six years for FEMA to nail down flood-zone boundaries when it should ideally take two.”

 

I ended up choosing this article because it perfectly relates with what we’ve been talking about in class. One of the ways I thought it related with our class is why we’re using tomorrow.io’s data. Like they said in the article, the current data we’re using to predict all of our weather data, not just for flooding, is outdated because it gather data from everywhere around the world and it isn’t as up to date as it is from tomorrow.io. If we were to update these models with this data today, it makes me question if the number of homeowners in flood zones would increase.

 

In class, we also went over how to graph these types of models. In relation to this article, it makes me wonder what categories these people would fall under for flood risk, like did for Ashville. For instance, would all of these people be at high risk? How many people are we missing that aren’t at high risk, but can be at risk in the future? All of these numbers can impact the lives of real people, and it’s sad to know that many of them aren’t even aware of it. Reading this article makes me wonder what other things American will be at risk to in the future once this forecasting data gets updated.

Jaiden Davey

Article: https://www.wsj.com/us-news/lood-zone-risk-maps-15da3709?mod=climate-environment_news_article_pos1 

‘Hidden costs’ of climate emergency are worsening California’s affordability crisis

In the past, glacial melting, rising sea levels, and far off dangers were the typical themes when climate change news was discussed. However, a recent article in The Guardian illustrates how the cost of climate change is already undermining the financial stability of the average American, with a major emphasis on California. The article "'Hidden costs' of climate emergency are worsening California's affordability crisis report" reports on a study by the Center for Law, Energy & Environment at the University of California, Berkeley made possible by Next 10 that conveys an ugly scenario: climate change is no longer a distant environmental problem but a predominant economic problem of the present time.

According to the report, climate change will be responsible for a typical American who lives from 2024 onwards to an extension of his/her life expenses by half a million dollars. Others may,cumulatively, have to face an increase in costs that sum up to 1 million of their lifetime. The "hidden costs" are the accumulations of impacts of extreme weather events such as wildfires, heatwaves, and storms. The article mentions that wildfires alone made a loss of production worth $4.6 billion in one month of 2025 and additionally caused income losses in California amounting to nearly $60 billion from 2017 to 2021. Destruction of property and infrastructure is only a part of these disasters. Decreased wages and productivity, increased healthcare costs due to diseases caused by smoke and heat, and higher utility prices are also some of the consequences of this chain that goes further and beyond the energy companies that have to adjust to the rising demand and pressure on the system.

The article represents climate change not only as a problem of the environment and ethics but also as an issue related to the people's wallets. The climate crisis is that which not only is already present in California but is also going to exacerbate the situation there, with housing, and utility costs already going up and the crisis deepening. The report exposes how it will be a hugely more expensive undertaking not to take action against climate change than that of paying for the costs of adaptation and mitigation simultaneously through the money spent now.

One of the article's strengths is its multi dimensionality. Rather than presenting one sided information, for instance, how insurance can be influenced, it shows how climate change leads to the gradual collapse of different systems simultaneously. Besides that, the article discusses that the impacts of climate change are not neutral, that is, the low income households, which to a great extent are already affected by unaffordability, do not have the capacity to absorb these hidden costs, thereby making climate change a serious matter of economic justice.

Nevertheless, these economic projections come with some reservations in tow. Assumptions about the intensity of future climate events, the speed of corporations' and governments' adaptation, and other more general economic trends are all factors that make climate projections and estimated costs reliant on those assumptions. Hence, the $500,000 total lifetime cost is more of an approximation than a definite fact. Further, even though California is an almost perfect example, the above mentioned impacts will not necessarily occur in different U.S. regions as the climate varies in these areas.

Despite these reservations, the article serves an important purpose in the education of the public. This is because it ties the affordability theme and economic security to climate action, thereby repositioning climate action as consistent with the protection of livelihoods and ecosystems. Policymakers are not only told that failure to act will become increasingly more expensive but also that the costs will be borne mostly by the least privileged. Climate change is not just altering the natural environment, but also the economy, and what is happening in California is only a mild glimpse of what's coming in the future.

Reference: https://www.theguardian.com/us-news/2025/sep/25/california-climate-emergency-affordability-crisis-study?utm_source=chatgpt.com

"Recently Emerging Trends in Big Data Analytic Methods for Modeling and Combating Climate Change Effects"

 

Fig. 2

    This literature review by Ikegwu et al. (2024) examines how big data analysis can help us understand and fight climate change. The paper gives thought to other ways of analyzing huge amounts of climate data and weighs the advantages and the disadvantages of each method. While the paper deals with a timely topic, there are certain issues regarding the research design and presentation.

    The article is dealing with a critically important topic. Climate change is one of the biggest problems we face today, and using big data to become more familiar with it is understandable. Ikegwu et al. (2024) effectively classify different types of data analytics methods and outline what each does. They also provide a useful summary of climate data sources, including satellites, weather stations, and computer models. The article is clearly organized with clear headings that make it easy to read. Ikegwu et al. (2024) tackle both traditional methods like statistical models and newer methods like machine learning and artificial intelligence. They also address real-world applications, such as disease prediction under climate change and monitoring of crop growth.

    However, one of the significant problems with this paper is that while it quotes many studies, the authors are not clear about what their search strategy was or how they chose the literature that they reviewed. The paper is not clear on how they found and chose the studies that they included, and it is therefore difficult to know if their review is exhaustive or if they left out key research (Ikegwu et al., 2024). Without a methodology section describing their search, databases, and inclusion criteria, readers cannot evaluate the reliability and comprehensiveness of their findings. The paper mainly describes several approaches but does not critically evaluate their performance. Although Ikegwu et al. (2024) list strengths and weaknesses in tables, they do not provide comprehensive analysis of when each approach works or does not work. For example, they describe machine learning approaches but do not compare how well each performs relative to traditional approaches through real data.

    Another concern is that the authors indicate their study covers a gap in the literature, but do not clearly show how they deliver something new. Much of the information seems to simply echo what is currently known about big data and climate change. The paper would be more persuasive if it described some of the problems existing approaches cannot solve or proposed new approaches. Though Ikegwu et al. (2024) discuss extremely numerous disparate topics, sometimes it is too nontechnical in specificity to be useful to researchers or practitioners. For instance, when the authors are referring to machine learning algorithms, they cite such techniques as support vector machines and decision trees without explaining when to use each of these or how each can be differentiated in climate contexts.

    The paper does not satisfactorily report data quality and validation issues. Climate data contain missing values, measurement flaws, and biases affecting analytical outputs (Ikegwu et al., 2024). These challenges are noted by the authors superficially, but they do not discuss how different analytics methods address these concerns. A few essential issues are omitted in this review. The article does not talk about the ethical issues of using big data in climate studies, like privacy problems when using social media or satellite images. It also does not mention the energy and resources needed for computing, which is surprising since the topic is about protecting the environment.

Fig. 3

    Ikegwu et al. (2024) are not providing enough information on how effective these methods have been in actual application. While they refer to some examples, they do not evaluate the results critically or address any limitations. To make this study more helpful, the authors must expand their literature search to encompass more sources and provide a clear study selection methodology. Ikegwu et al. (2024) could do better by showing more critical comparison among different methodologies, such as their efficacy and scopes of knowledge gaps. The review would be further enriched by having more reference to practical issues, such as data amalgamation from different sources and computational capability involved for different strategies. Adding case studies with achievements and mishaps would make the review more well-rounded and valuable.

While this paper addresses an important theme and offering a useful summary of the big data strategy for climate science, it is not a full literature review. The limited range, lack of critique, and omission of methodology details make it less useful for researchers and practitioners. The topic calls for more comprehensive and rigorous treatment to enable the improved direction of future research and applied work in this important area.

References

Ikegwu, A. C., Nweke, H. F., Mkpojiogu, E., Anikwe, C. V., Igwe, S. A., & Alo, U. R. (2024). Recently emerging trends in big data analytic methods for modeling and combating climate change effects. Energy Informatics, 7(1), 1-28. https://doi.org/10.1186/s42162-024-00307-5

China’s Pivot From Green Tech Could Be Bad News For The Climate

The article, “China’s Pivot From Green Tech Could Be Bad News For The Climate,” by Wallace-Wells, makes a bigger point that goes beyond the environment, clean energy isn’t just about fighting climate change anymore, it’s also about global power. China is miles ahead in solar, batteries, and electric cars. In fact, some American commentators who once talked about competing with China are now sounding almost impressed by its progress. Investors are starting to admit it’s nearly impossible to match China’s scale, and even tough debates in Washington about blocking high-tech exports are starting to fade. Wallace-Wells warns that this rivalry could turn into a new kind of Cold War centered on energy and the climate. 

Some critiques I have of this opinion piece is that, for one, the idea that China is suddenly pulling back from clean energy may be overblown. Even if the pace slows, the country is still adding massive amounts of renewable power. And China has strong reasons to keep pushing forward, like reducing pollution, protecting its energy security, and creating jobs, so it’s unlikely to just walk away. The “what if they stop?” question makes the situation sound worse than it really is.

Another issue is the way he frames China as either the world’s hero or its villain. The reality is more complicated. China’s choices are shaped by business pressures, political strategy, and national interests, not by a desire to save the planet. And other countries aren’t sitting still either, European countries, India, and even at one point, the U.S. are all investing in renewables. Acting like everything depends on China overlooks these efforts.

But in my opinion, Wallace-Wells has a crucial point, the U.S. and other Western countries have been too slow. For too long, they treated climate action as a moral stance instead of an industrial race, while China built the factories and grabbed the lead. Now, whether China speeds up or slows down, the West has to face the reality that it needs to build its own capacity, or risk relying on China forever.

The article left me with mixed feelings. On one hand, it’s worrying to see climate progress so tied up with global competition, because that tension could slow things down when we can least afford it. On the other hand, it’s motivating. The solution isn’t unattainable, countries just need to treat climate leadership as real investment in clean industries, not just talk. Wallace-Wells’s question, “What if China stops?” is a challenge to the rest of the world. 

Reference: China’s Pivot From Green Tech Could Be Bad News for the Climate: David Wallace-Wells, https://www.proquest.com/nytimes/docview/3253846119/fulltext/5D7BBD3F37424255PQ/1?accountid=12164&sourcetype=Blogs,%20Podcasts,%20&%20Websites

"How the Anti-Green Agenda Could Help Climate Tech"

    Vinod Khosla’s recent piece in The Economist, “The greenlash’s silver lining,” takes a different angle on climate technology than most articles I’ve read. Instead of focusing on politics or environmental arguments, he frames climate tech as a tool of economic power. He argues that America should invest in new climate technologies to cut emissions and stay competitive with countries like China and India. The point isn’t whether people believe in climate change or not, it’s that whoever dominates these industries will hold enormous global influence.

    The main idea he pushes is what he calls the “Chindia price.” If a technology like fusion, geothermal, or green cement becomes cheaper than fossil fuels, then China and India will adopt it without subsidies, and that’s when global change really happens. He compares this to solar manufacturing, where China already controls more than 80% of the global market. That dominance didn’t just make solar cheaper worldwide; it also gave China leverage similar to OPEC’s power in oil markets. His concern is that the U.S. is spending too much money subsidizing mature tech like solar and wind instead of pushing harder on newer, game-changing technologies that could set America apart.

    Khosla lays out some examples of where breakthroughs might come from. He highlights American startups working on nuclear fusion, which could one day replace coal plant boilers and turbines with fusion systems while keeping the rest of the infrastructure in place. He also points to “super hot” geothermal, which could tap into extreme underground heat and compete with natural gas. On the industrial side, he believes steel and cement can be produced with lower emissions at costs equal to or even cheaper than today’s methods. These are not just scientific ideas but business opportunities that could make the U.S. less dependent on imports and stronger in exports.

    I thought his take was both practical and controversial. On one hand, he’s right that technology won’t matter unless it scales and becomes affordable. Fusion and geothermal are exciting examples. If they can work at scale, they could completely change the energy picture. I also liked his point about subsidies needing to be temporary and targeted. It makes sense that once a technology is mature, like solar, it should stand on its own without government support. However, I wasn’t convinced by how quickly he brushed off the idea of climate justice. It feels incomplete to say that equity doesn’t matter, because the communities most affected by pollution and climate change can’t wait for markets to sort things out. Policy has to balance cost and competitiveness with fairness.

    This connects well to what we’re learning in class about climate technology and analytics. Khosla argues that data on cost, adoption, and scale should drive decisions not politics, with his “Chindia price” serving as a kind of threshold model: once clean tech is cheaper than fossil fuels, global adoption follows. That raises important questions about which technologies are closest to this tipping point, and how we calculate real costs when infrastructure and risks are factored in. While I don’t agree with all his points, the essay reframed climate progress as an economic race where both cost and fairness must matter.

Reference:

Vinod Khosla on how the anti-green agenda could help climate tech

"Climate Tech Atlas Could Unlock Net Zero Breakthroughs"

  

Climate change is an urgent matter with a race against time to mitigate the disastrous consequences it will have on the world. Technology serves as the cornerstone of net-zero emissions strategies, acting as a catalyst for innovation across sectors that can transform the response toward climate change. Climate Tech Atlas created a dynamic online platform program to map out the promising technological innovations that can help reach the goal of being net-zero emission by 2050. This platform is strategically reshaping different sectors by mapping out both near-term imperatives and long-term moonshots. It provides insight into evaluating and navigating climate technologies, especially in areas where uncertainty plagues strategic decisions.

The policy and economic sector is an area that can reshape the framework in which strategic decisions are made regarding climate change. The uncertainty of not being able to know which technological advancement can lead to reaching the net-zero goal affects how those two sectors frame their decision-making. With the uncertainty of it all, it stalls investment and regulatory action that allows for adaptive advancement. Providing a new framework in which uncertainty is not the driving force in the regulatory decision-making for continuous improvement in climate change. It creates the opportunity for investment in new markets and opens new job creation opportunities. The mapping of promising technological innovations can allow policymaking to be adaptive toward innovative technology while fostering a sustainable climate in the future.

However, without transparency of the data, it leaves room for questioning the bias of the data analysis. Without any details on the methodology behind the modeling, it raises concerns about how or what is used to create the projections. It refers to emissions modeling for their mapping of the promising technological innovation, without any details on where the data came from, such as open-source climate modeling or expert sources. In addition, its categorization of imperative or moonshot technologies has no details on which standard they are using to categorize them. Transparency is a key factor in striving for the net-zero goal, as it allows for strategic decision-making to be made and trust. For instance, investors need to be able to know how the projection was created to have the confidence to invest in a new market. A politician must understand where the data and methodology are used to implement the legislation.

A great aspect of the platform is how Climate Tech Atlas took a complex topic of emissions and divided it into different sectors, with subdivisions of each sector alongside the amount of emissions produced by 2050. This helps an investor or policymaker to easily identify the most promising technology for each sector. Sector-specific modeling allows for effective policy design, such as tailoring incentives for each specific sector within the government. The strategic categorization of technology facilitates the vision of immediate action and long-term vision. It offers a dual perspective into what is effective today, while also fostering the vision of what potential technological innovation can be, the pivotal factor to achieving the goal in the future.

Reference 

Climate Tech Atlas Could Unlock Net Zero Breakthroughs

"Charleston Floods Are Getting Worse. For These Residents, It’s Worth the Risk."

 The article I chose to go with for my first blog is “Charleston Floods Are Getting Worse. For These Residents, It’s Worth the Risk.” from the Wall Street Journal. I thought this article relates perfectly to our class because it discusses the impact of climate change and severe weather on a historical town, both in physical damage and damage in property value.

 

To give some background, the article highlights that Charleston is known for being a beautiful and historic town that is on a peninsula. When people first started to move there, they originally chose to live up on elevated ground, only for bodies of water like marshes, ponds, and streams to come in as well. Because of the surrounding bodies of water, the town is very susceptible to flooding. One of the most telling statistics they mention in the article about the recent climate change is that “73% of the major ocean floods to hit the city between 1923 and 2024 have occurred since 2015.” Unfortunately, as the title suggests, the flooding is only going to get worse for this town. However, despite the clear signs for upcoming property damage, the article informs that the market for these homes are doing great simply because of how beautiful the historic and Victorian style homes are. One of the most interesting quotes I found from the article about this was “many of the city’s sought-after historic homes have been raised to help keep water out of the living areas, which costs a minimum of $500,000.”

 

Overall, I thought the people living in this area are nuts. I’d understand, maybe, if these were beach properties, but these are people’s actual homes that that they live in year round. What will they do if the flooding destroys their homes? What if they can’t get insurance to cover them? From the sound of it, they seem to think it’s worth it for the historical and scenic aspects, but I can’t help but question why they would potentially put their lives on the line to be able to hold on to that. I think if we were to look at the data of flooding in this area, I would want to know what happens if the flooding got worse. If a really bad flood comes through and destroys many of the properties, how much value would be lost and how much could it potentially cost to repair it? Another thing I would want to know is how effective are the protective measurements they’re putting in are. If they’re spending a minimum of $500,000 to raise these homes, what are they using to measure how effective they would be.

 

Personally, never in a million years would I invest in any of these homes, but I understand to some degree why people would choose to live in this area. From this article, I am curious what others think of the true value of these homes. Should the homes be worth this much or not?

 

Thanks!

 

Jaiden Davey

Article: https://www.wsj.com/real-estate/luxury-homes/charleston-sc-flooding-c492c3e5?mod=climate-environment_more_article_pos2 

The Use of AI in Combating Climate Change

 

The Role of AI in Tackling Climate Change highlights how artificial intelligence can be applied to areas including climate modeling and forecasting, energy efficiency, food production, biodiversity protection, and carbon capture and climate mitigation. As someone who is fascinated with AI, I find all the opportunities that emerging technologies revolved around them and how they can better humanity to be intriguing.

Everyday there are debates on whether or not AI is worth energy consumption for everyday use. From the carbon footprint involved in mining for manufacturing to the water and energy used in running and cooling the systems, using AI is often harmful to the environment. However, it is often overlooked at the potential upside these models have.

Through machine learning techniques, AI allows for more accurate predictions of climate trends. With climate temperatures increasing year after year, causing more devastating weather events such as floods, hurricanes, droughts and wildfires; AI acts as an early warning system for different natural disasters. Allowing for better preparation and saving lives. AI is being used to hopefully slow down the acceleration of climate change by finding the most efficient and stable materials in storing CO2 captured from emissions. As good as this sounds I wonder if it is too late or not. As increasing global temperatures require reaction and funding from governments whose main focus appears to be on things like the economy, war and ever-growing di

vision.  If this continues to be, it may be advantageous for humanity to adjust in certain ways.

One thing that is certain is that natural disasters are on the rise. AI is already helping with early predictions and warning for these disasters. However, what happens when the disaster strikes key agricultural areas? With droughts it requires more and more water just to grow crops. Hurricanes, tornadoes and floods can wipe out entire fields. Indoor vertical farming and AI may be one safe measure against mother nature.

Indoor vertical farming has been around for a few decades; however, it has not been able to turn profits. It isn’t the around-the-clock energy that has made the business unprofitable. Instead, it is the blue-collar workers, capital expenditure and maintenance that drives the unstable business model. In total these big three make up over 80 percent of the total cost of producing food. In this case it costs 6x more to produce a head of lettuce through indoor vertical agriculture than is through traditional agriculture methods.

Perhaps AI can be used to create a sustainable business model in vertical farming. With the help of AI, the number of blue-collar workers could be reduced. AI models could be developed to predict maintenance needed on equipment and with new technologies the price of capital expenditure may also be reduced. It isn’t too far-fetched to say that with AI, indoor vertical agricultural may be sustainable. Creating a world where produce is grown locally, in farms that are a fraction of the size of traditional agricultural farms and more efficient.

Welcome to our course on Climate Technology and Analytics at Loyola University. This is a unique course that blends weather data with maps and other sources of information to better understand implications for financial services, notably for property insurance.

The goal of this blog is to capture your thoughts on news or magazine articles that speak directly to what we are studying. Blogs provide a collaborative forum for students (and those outside the class who share an interest in this subject) to share stories and insights regarding the world of information systems with a particular focus on climate analytics. You can find any article anywhere or articles if you prefer - but please try to look for something that is recent - and build a 500-word blog post around it.

Over the course of the next few weeks, students will be posting items of interest to this blog. The blog is open to the public - they can read but only students enrolled in the course are permitted to post to the blog.

If you have any doubts as to whether the article you want to blog about is relevant, please speak with me ahead of time. Students are expected to pay close attention to what their peers have posted on the blog and to make comments on what they read. The use of this blog will hopefully prove to be a valuable learning tool for everyone in the class. By sharing our knowledge with one another through an open and interactive forum, we can learn much more both individually and as a class. Please ensure that whatever materials you post to the blog are appropriately cited. If you find an article on the web which you would like to bring to our attention, please post the exact URL with reference to where the article has come from.

Thanks everyone - let the blogging commence!

Paul