‘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.