#climate research

[reasonsforhope]

"Amid record-high temperatures, devastating disasters, and the resulting climate anxiety that comes with them, it can be easy to give in to despair. 

The resounding question of “does this even matter?” likely echoes on a loop, every time you toss an item in the recycling bin, or call your elected officials for the umpteenth time.

But according to research from the University of California San Diego’s School of Global Policy and Strategy, public outcry can indeed lead to significant environmental action — even when public officials are openly hostile to climate-forward policies.

Their paper, titled “Going Viral: Public Attention and Environmental Action in the Amazon,” will soon be published in the Journal of the Association of Environmental and Resource Economists. It focuses on the “unprecedented” public scrutiny following forest fires in the Brazilian Amazon in August of 2019.

These fires occurred soon after Jair Bolsonaro became Brazil’s president, after a staunchly anti-environmental campaign.

But after analyzing both media coverage and international pressure towards Brazil’s federal government, the researchers found that the increased public attention resulted in a 22% decrease in fires in the country’s Amazon Rainforest. 

This, in turn, translated into the avoidance of an estimated 24.8 million tons of CO2 emissions.

“Our research underscores the significant role that public attention and media coverage can play in influencing local environmental policies and actions,” the study’s coauthor Teevrat Garg, said in a statement...

“The 2019 surge in attention led to immediate governmental responses, which contributed to the notable decrease in fires,” he added.

To come to these conclusions, the researchers compared fire activity in Brazil with that in Peru and Bolivia, countries that did not receive the same amount of public pressure, though typically still have the same level of fire activity per square kilometer."

-via GoodGoodGood, October 4, 2024

[rjzimmerman]

Excerpt from this story from Inside Climate News:

In the most extensive analysis of its kind, new research suggests that fossil fuel influence is widespread across universities in the United States, United Kingdom, Canada and Australia. 

Oil and gas companies have poured funding into campuses for decades. But scientists, journalists and students are only just starting to uncover the true extent of these financial ties—and how potential conflicts of interest in higher education could hinder efforts to combat climate change, the study’s authors say. 

“It’s a really troubling lack of transparency that kind of has created this situation where people have been trying to pull back the curtain on some of this, but struggling because a lot of this data just is not in the public domain,” study co-author Geoffrey Supran, an associate professor of environmental science and policy at the University of Miami, told me. “We observe that fossil fuel companies have embedded themselves widely within universities.” 

In the past few years, student activists have increasingly pushed their universities to divest from oil and gas on campus and in investment portfolios. Now, this movement is trickling into the university research community amid a growing push to increase transparency of fossil fuel funding sources—and potentially cut ties altogether. 

Supran noted that “conflicts of interest are not necessarily implied bias.” However, a 2022 study published in the journal Nature Climate Change found university research centers funded by fossil fuel companies were more supportive of natural gas than those that are not. 

The new study finds a dearth of research investigating other potential ways that fossil fuel funding can influence climate research. As part of their work, the scientists parsed through around 14,000 peer-reviewed articles about conflicts of interest, bias and research funding across all industries. Just seven discussed fossil fuels. 

[solarpunkpresentspodcast]

As the changing climate increasingly disrupts our ways of life, we have three choices: do nothing, attempt to stop or even reverse climate change, and/or figure out how to withstand it. Option one is a terrible idea and the ship has (mostly) sailed on option two. But option three is how we learn to live—and maybe even thrive—in our changing world. Part of this is figuring out how to convey the information that climate researchers have gathered to the people—like farmers, water managers, and urban planners—who need to make decisions now—about things like what crops to plant, where to get water for everyone and how to allocate it, and where to plant trees—for both the near and slightly distant future. In this episode, we’re talking to Professor Lisa Dilling, of the University of Colorado, Boulder, about building networks of people through which information about regional climate predictions can flow to people and information about the needs, predicaments, and questions of people can flow to climate researchers.

You can follow Lisa Dilling on Twitter at @LisaD144, and the Western Water Assessment program at University of Colorado here: @WWAnews or visit their website at https://wwa.colorado.edu/

Connect with Solarpunk Magazine at solarpunkmagazine.com and on Twitter @solarpunklitmag

Connect with Solarpunk Presents Podcast on Twitter @SolarpunkP, Mastodon @solarpunkpresents@climatejustice.rocks, or at our blog https://solarpunkpresents.com/

Connect with Ariel at her blog, on Twitter at @arielletje, and on Mastodon @arielkroon@wandering.shop

Connect with Christina at her blog, on Twitter @xtinadlr, and on Mastodon @xtinadlr@wandering.shop

[elsa16744]

Data Analytics in Climate Change Research | SG Analytics 

Corporations, governments, and the public are increasingly aware of the detrimental impacts of climate change on global ecosystems, raising concerns about economic, supply chain, and health vulnerabilities. 

Fortunately, data analytics offers a promising approach to strategize effective responses to the climate crisis. By providing insights into the causes and potential solutions of climate change, data analytics plays a crucial role in climate research. Here’s why leveraging data analytics is essential: 

The Importance of Data Analytics in Climate Change Research 

Understanding Complex Systems 

Climate change involves intricate interactions between natural systems—such as the atmosphere, oceans, land, and living organisms—that are interconnected and complex. Data analytics helps researchers analyze vast amounts of data from scholarly and social platforms to uncover patterns and relationships that would be challenging to detect manually. This analytical capability is crucial for studying the causes and effects of climate change. 

Informing Policy and Decision-Making 

Effective climate action requires evidence-based policies and decisions. Data analytics provides comprehensive insights that equip policymakers with essential information to design and implement sustainable development strategies. These insights are crucial for reducing greenhouse gas emissions, adapting to changing conditions, and protecting vulnerable populations. 

Enhancing Predictive Models 

Predictive modeling is essential in climate science for forecasting future climate dynamics and evaluating mitigation and adaptation strategies. Advanced data analytics techniques, such as machine learning algorithms, improve the accuracy of predictive models by identifying trends and anomalies in historical climate data. 

Applications of Data Analytics in Climate Change Research 

Monitoring and Measuring Climate Variables 

Data analytics is instrumental in monitoring climate variables like temperature, precipitation, and greenhouse gas concentrations. By integrating data from sources such as satellites and weather stations, researchers can track changes over time and optimize region-specific monitoring efforts. 

Assessing Climate Impacts 

Analyzing diverse datasets—such as ecological surveys and health statistics—allows researchers to assess the long-term impacts of climate change on biodiversity, food security, and public health. This holistic approach helps in evaluating policy effectiveness and planning adaptation strategies. 

Mitigation and Adaptation Strategies 

Data analytics supports the development of strategies to mitigate greenhouse gas emissions and enhance resilience. By analyzing data on energy use, transportation patterns, and land use, researchers can identify opportunities for reducing emissions and improving sustainability. 

Future Directions in Climate Data Analytics 

Big Data and Edge Computing 

The increasing volume and complexity of climate data require scalable computing solutions like big data analytics and edge computing. These technologies enable more detailed and accurate analysis of large datasets, enhancing climate research capabilities. 

Artificial Intelligence and Machine Learning 

AI and ML technologies automate data processing and enhance predictive capabilities in climate research. These advancements enable researchers to model complex climate interactions and improve predictions of future climate scenarios. 

Crowdsourced Datasets 

Engaging the public in data collection through crowdsourcing enhances the breadth and depth of climate research datasets. Platforms like Weather Underground demonstrate how crowdsourced data can improve weather forecasting and climate research outcomes. 

Conclusion 

Data analytics is transforming climate change research by providing innovative tools and deeper insights into sustainable climate action. By integrating modern analytical techniques, researchers can address significant global challenges, including carbon emissions and environmental degradation. As technologies evolve, the integration of climate research will continue to play a pivotal role in safeguarding our planet and promoting a sustainable global ecosystem. 

[alpaca-clouds]

Also don't forget that in some of the studies up to 20% of the participants instantly went to get a better education degree of some sort. And people getting better educated is always a good thing, because educated people can more effectively help us solve problems like climate change. A problem, mind you, in which the research is still very much underfunded. And while yes, some of the research cost is always technological stuff (be it computers, be it lab equipment, be it other stuff) a lot of research costs is labor costs. Right now a ton of researchers do their research in terms of climate change and how to prevent it at least partly for free in their free time. Now imagine how much that percentage would go up, if people actually had the money to afford more free time!

My least favorite things about anti- UBI discourse is always the techbros whining that "nobody is going to work anymore! People will just watch Netflix all day!" and I have 2 responses:

1) Who the fuck cares. Who the fuck cares what people do with their time! That's kind of the fucking point!

2) People aren't going to stop laboring. Housework (look, it's right there in the word!) will still need to be done. So will maintenance on our homes and personal spaces. Children will still need carers, as will the elderly and disabled. There are millions of examples of ~work~ that we do all the time, uncompensated, that won't suddenly stop because we aren't forced to sell our labor to provide corporation's profits.

I'm not surprised that what is traditionally women's work is invisible to these dipshits, but it never fails to anger me.

Anyway. Join the IWW.

[trendynewsnow]

Europe's Climate Crisis: Urgent Actions Needed

Europe’s Climate Challenges: A Call to Action From the devastating floods that have ravaged parts of Spain to the relentless droughts plaguing Sicily, and from the rapidly melting Alpine glaciers to the waterlogged agricultural fields in France, Europe is facing an escalating climate crisis. Each individual has their own unique climate narrative, but the overarching urgency for action is becoming…

[jcmarchi]

Revealing causal links in complex systems

New Post has been published on https://thedigitalinsider.com/revealing-causal-links-in-complex-systems/

Revealing causal links in complex systems

Getting to the heart of causality is central to understanding the world around us. What causes one variable — be it a biological species, a voting region, a company stock, or a local climate — to shift from one state to another can inform how we might shape that variable in the future.

But tracing an effect to its root cause can quickly become intractable in real-world systems, where many variables can converge, confound, and cloud over any causal links.

Now, a team of MIT engineers hopes to provide some clarity in the pursuit of causality. They developed a method that can be applied to a wide range of situations to identify those variables that likely influence other variables in a complex system.

The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region).

The engineers then generate a “causality map” that links variables that likely have some sort of cause-and-effect relationship. The algorithm determines the specific nature of that relationship, such as whether two variables are synergistic — meaning one variable only influences another if it is paired with a second variable — or redundant, such that a change in one variable can have exactly the same, and therefore redundant, effect as another variable.

The new algorithm can also make an estimate of “causal leakage,” or the degree to which a system’s behavior cannot be explained through the variables that are available; some unknown influence must be at play, and therefore, more variables must be considered.

“The significance of our method lies in its versatility across disciplines,” says Álvaro Martínez-Sánchez, a graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “It can be applied to better understand the evolution of species in an ecosystem, the communication of neurons in the brain, and the interplay of climatological variables between regions, to name a few examples.”

For their part, the engineers plan to use the algorithm to help solve problems in aerospace, such as identifying features in aircraft design that can reduce a plane’s fuel consumption.

“We hope by embedding causality into models, it will help us better understand the relationship between design variables of an aircraft and how it relates to efficiency,” says Adrián Lozano-Durán, an associate professor in AeroAstro.

The engineers, along with MIT postdoc Gonzalo Arranz, have published their results in a study appearing today in Nature Communications.

Seeing connections

In recent years, a number of computational methods have been developed to take in data about complex systems and identify causal links between variables in the system, based on certain mathematical descriptions that should represent causality.

“Different methods use different mathematical definitions to determine causality,” Lozano-Durán notes. “There are many possible definitions that all sound ok, but they may fail under some conditions.”

In particular, he says that existing methods are not designed to tell the difference between certain types of causality. Namely, they don’t distinguish between a “unique��� causality, in which one variable has a unique effect on another, apart from every other variable, from a “synergistic” or a “redundant” link. An example of a synergistic causality would be if one variable (say, the action of drug A) had no effect on another variable (a person’s blood pressure), unless the first variable was paired with a second (drug B).

An example of redundant causality would be if one variable (a student’s work habits) affect another variable (their chance of getting good grades), but that effect has the same impact as another variable (the amount of sleep the student gets).

“Other methods rely on the intensity of the variables to measure causality,” adds Arranz. “Therefore, they may miss links between variables whose intensity is not strong yet they are important.”

Messaging rates

In their new approach, the engineers took a page from information theory — the science of how messages are communicated through a network, based on a theory formulated by the late MIT professor emeritus Claude Shannon. The team developed an algorithm to evaluate any complex system of variables as a messaging network.

“We treat the system as a network, and variables transfer information to each other in a way that can be measured,” Lozano-Durán explains. “If one variable is sending messages to another, that implies it must have some influence. That’s the idea of using information propagation to measure causality.”

The new algorithm evaluates multiple variables simultaneously, rather than taking on one pair of variables at a time, as other methods do. The algorithm defines information as the likelihood that a change in one variable will also see a change in another. This likelihood — and therefore, the information that is exchanged between variables — can get stronger or weaker as the algorithm evaluates more data of the system over time.

In the end, the method generates a map of causality that shows which variables in the network are strongly linked. From the rate and pattern of these links, the researchers can then distinguish which variables have a unique, synergistic, or redundant relationship. By this same approach, the algorithm can also estimate the amount of “causality leak” in the system, meaning the degree to which a system’s behavior cannot be predicted based on the information available.

“Part of our method detects if there’s something missing,” Lozano-Durán says. “We don’t know what is missing, but we know we need to include more variables to explain what is happening.”

The team applied the algorithm to a number of benchmark cases that are typically used to test causal inference. These cases range from observations of predator-prey interactions over time, to measurements of air temperature and pressure in different geographic regions, and the co-evolution of multiple species in a marine environment. The algorithm successfully identified causal links in every case, compared with most methods that can only handle some cases.   

The method, which the team coined SURD, for Synergistic-Unique-Redundant Decomposition of causality, is available online for others to test on their own systems.

“SURD has the potential to drive progress across multiple scientific and engineering fields, such as climate research, neuroscience, economics, epidemiology, social sciences, and fluid dynamics, among others areas,” Martínez-Sánchez says.

This research was supported, in part, by the National Science Foundation.

[michaelgabrill]

Meloë Kacenelenbogen Eyes the Future of Air Quality Climate Research

A mentor of research scientist Meloë Kacenelenbogen once shared a sentiment from French author André Gide: “You cannot discover new oceans unless you have the courage to lose sight of the shore.” Kacenelenbogen pushes beyond her comfort zone to explore the unknown. Name: Meloë S. KacenelenbogenFormal Job Classification: Research scientistOrganization: Climate and Radiation Laboratory, Science […] from NASA https://ift.tt/gl7QZB6

[delicatelysublimeforester]

The Dance of Weather: How Trees and Forests Shape Our World

[lifeinfleeting]

Why people skip climate protest?: Insights from Recent Research

Photo: Alejandro Alvarez, CC BY-SA 4.0, via Wikimedia Commons In the fight against climate change, climate protests, such as those driven by the Fridays for Future movement, are pivotal in pushing for more ambitious climate policies. However, new research published in Nature Climate Change reveals surprising phenomenon in protest participation. This phenomenon, called “strategic…

[sniperct]

NOAA “should be dismantled and many of its functions eliminated, sent to other agencies, privatized, or placed under the control of states and territories,” Project 2025 reads. The proposals roughly amount to two main avenues of attack. First, it suggests that the NWS should eliminate its public-facing forecasts, focus on data gathering, and otherwise “fully commercialize its forecasting operations,” which the authors of the plan imply will improve, not limit, forecasts for all Americans. Then, NOAA’s scientific-research arm, which studies things such as Arctic-ice dynamics and how greenhouse gases behave (and which the document calls “the source of much of NOAA’s climate alarmism”), should be aggressively shrunk. “The preponderance of its climate-change research should be disbanded,” the document says. It further notes that scientific agencies such as NOAA are “vulnerable to obstructionism of an Administration’s aims,” so appointees should be screened to ensure that their views are “wholly in sync” with the president’s.

Climate change is the single most important issue in the entirety of human history. One party wants to fight it. The other party wants to let the earth burn; in fact, they want to accelerate the fire.

[phildumphy]

So it turns out that ChatGPT not only uses a ton shit of energy, but also a ton shit of water. This is according to a new study by a group of researchers from the University of California Riverside and the University of Texas Arlington, Futurism reports.

Which sounds INSANE but also makes sense when you think of it. You know what happens to, for example, your computer when it’s doing a LOT of work and processing. You gotta cool those machines.

And what’s worrying about this is that water shortages are already an issue almost everywhere, and over this summer, and the next summers, will become more and more of a problem with the rising temperatures all over the world. So it’s important to have this in mind and share the info. Big part of how we ended up where we are with the climate crisis is that for a long time politicians KNEW about the science, but the large public didn’t have all the facts. We didn’t have access to it. KNOWING about things and sharing that info can be a real game-changer. Because then we know up to what point we, as individuals, can have effective actions in our daily lives and what we need to be asking our legislators for.

And with all the issues AI can pose, I think this is such an important argument to add to the conversation.

Edit: I previously accidentally typed Colorado instead of California. Thank you to the fellow user who noticed and signaled that!

[reality-detective]

Trump: "One of the most urgent tasks... is to decisively defeat the climate hysteria hoax."

"The radical left's fearmongering about climate and our future is... destroying America's economy, weakening our society, and eviscerating our middle class. It's really hurting us."

"We have to defeat the climate hoaxsters once and for all." 🤔

[reasonsforhope]

By the way, I would literally bet money that we're going to successfully keep global warming below 2 degrees celsius.

Would I bet my whole savings on it? No, not yet. But the way the data is trending, in two or three years, I very well might.