Bowdoin Science Journal

climate change

AI – save or ruin the environment?

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With the fast speed that AI is currently developing, it has the potential to alleviate one of the most pressing problems—climate change. AI applications, such as smart electricity grids and sustainable agriculture, are predicted to mitigate environmental issues. On the flip side, the integration of AI in this field can also be counterproductive because of the high energy demand of the systems. If AI helps us to transition to a more sustainable lifestyle, the question is, at what cost?

The last decade saw exponential growth in data demand and the development of Large Language Models (LLMs)–computational models such as ChatGPT, designed to generate natural language. The algorithms resulted in increased energy consumption because of the big data volumes and computational power required, as well as increased water consumption needed to refrigerate data centers with that data. This consequently leads to higher greenhouse gas emissions (Fig.1). For example, the training of GPT-3 on a 500 billion-word database produced around 550 tons of carbon dioxide, equivalent to flying 33 times from Australia to the UK [1]. Moreover, information and communications technology (ICT) accounts for 3.9% of global greenhouse gas emissions (surpassing global air travel) [2]. As the number of training parameters grows, so does the energy consumption. It is expected to reach over 30% of the world’s total energy consumption by 2030. These environmental concerns about AI implementation led to a new term—Green AI.

Fig 1: CO2 equivalent emissions for training ML models (blue) and real-life cases (violet). In brackets, the billions of parameters are adjusted for each model [3].

Green algorithms are defined in two ways: green-in and green-by AI (Fig. 2). Algorithms that support the use of technology to tackle environmental issues are referred to as green-by AI. Green-in-design algorithms (green-in AI), on the other hand, are those that maximize energy efficiency to reduce the environmental impact of AI. 

 

Fig. 2. Overview of green-in vs. green-by algorithms.

 

Green-by AI has the potential to reduce greenhouse gas emissions by enhancing efficiency across many sectors, such as agriculture, biodiversity management, transportation, smart mobility, etc. 

  • Energy Efficiency. Machine Learning (ML) algorithms can optimize heating, air conditioning, and lighting by analyzing the data from the smart buildings, making them more energy efficient [4][5]. 
  • Smart Mobility. AI can predict and avoid traffic congestion by analyzing the current traffic patterns and optimizing routes. Moreover, ML contributes to Autonomous Vehicles by executing tasks like road following and obstacle detection, which improves overall road safety [6].
  • Sustainable agriculture. Data from sensors and satellites analyzed by ML can give farmers insights into crop health, soil conditions, and irrigation needs. This enables them to use the resources with precision and reduce environmental impacts. Moreover, predictive analytics minimize crop loss by allowing farmers to aid the diseases on time [7].
  • Climate Change. Computer-vision technologies can detect methane leaks in gas pipes, reducing emissions from fossil fuels. AI also plays a crucial role in reducing electricity usage by predicting demand and supply from solar and wind power.
  • Environmental Policies. AI’s ability to process data, identify trends, and predict outcomes will enable policymakers to come up with effective strategies to combat environmental issues [8].

Green-in AI, on the other hand, is an energy-efficient AI with a low carbon footprint, better quality data, and logical transparency. To ensure people’s trust, it offers clear and rational decision-making processes, thus also making it socially sustainable. Several promising approaches to reaching the green-in AI include algorithm, hardware, and data center optimization. Specifically, more efficient graphic processing units (GPUs) or parallelization (distributing computation among several processing cores) can reduce the environmental impacts of training AI. Anthony et al. proved that increasing the number of processing units to 15 will decrease greenhouse gas emissions [9]. However, the reduction in runtime must be significant enough for the parallelization method not to become counterproductive (when the execution time reduction is smaller than the increase in the number of cores, the emissions deteriorate). Other methods include computation at the locations where the data is collected to avoid data transmissions and limit the number of times an algorithm is run. 

Now that we know about AI’s impact and the ways to reduce it, what trends can we expect in the future? 

  • Hardware: Innovation in hardware design is focused on creating both eco-friendly and powerful AI accelerators, which can minimize energy consumption [10].
  • Neuromorphic computing is an emerging area in the computing technology field, aiming to create more efficient computing systems. It draws inspiration from the human brain, which performs complex tasks with much less energy than conventional computers. 
  • Energy-harvesting AI devices. Researchers are exploring the ways in which AI can harvest energy from its surroundings, for example from the lights or heat [11]. This way, AI can rely less on external power and become self-sufficient.

In conclusion, while AI holds great potential in alleviating many environmental issues, we should not forget about its own negative impact. While training AI models results in excessive greenhouse gas emissions, there are many ways to reduce energy consumption and make AI more environmentally friendly. Although we discussed several future trends in green-in AI, it is important to remember this field is still continuously evolving and new innovations will emerge in the future.

References:

[1] D. Patterson, J. Gonzalez, Q. Le, C. Liang, L.-M. Munguia, D. Rothchild, D. So, M. Texier, J. Dean, Carbon emissions and large neural network training, 2021, arXiv:2104.10350.

[2] Bran, Knowles. “ACM TCP TechBrief on Computing and Carbon Emissions.” Association for Computing Machinery, Nov. 2021  www.acm.org/media-center/2021/october/tpc-tech-brief-climate-change  

[3] Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark, “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024. 

[4] N. Milojevic-Dupont, F. Creutzig, Machine learning for geographically differentiated climate change mitigation in urban areas, Sustainable Cities Soc. 64 (2021) 102526.

[5] T.M. Ghazal, M.K. Hasan, M. Ahmad, H.M. Alzoubi, M. Alshurideh, Machine learning approaches for sustainable cities using internet of things, in: The Effect of Information Technology on Business and Marketing Intelligence Systems, Springer, 2023, pp. 1969–1986.

[6] M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L.D. Jackel, M. Monfort, U. Muller, J. Zhang, et al., End to end learning for self-driving cars, 2016, arXiv preprint arXiv:1604.07316. 

[7] R. Sharma, S.S. Kamble, A. Gunasekaran, V. Kumar, A. Kumar, A systematic literature review on machine learning applications for sustainable agriculture supply chain performance, Comput. Oper. Res. 119 (2020) 104926.

[8] N. Sánchez-Maroño, A. Rodríguez Arias, I. Lema-Lago, B. Guijarro-Berdiñas, A. Dumitru, A. Alonso-Betanzos, How agent-based modeling can help to foster sustainability projects, in: 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES, 2022.

[9] L.F.W. Anthony, B. Kanding, R. Selvan, Carbontracker: Tracking and predicting the carbon footprint of training deep learning models, 2020, arXiv preprint arXiv:2007.03051. 

[10] H. Rahmani, D. Shetty, M. Wagih, Y. Ghasempour, V. Palazzi, N.B. Carvalho, R. Correia, A. Costanzo, D. Vital, F. Alimenti, et al., Next-generation IoT devices: Sustainable eco-friendly manufacturing, energy harvesting, and wireless connectivity, IEEE J. Microw. 3 (1) (2023) 237–255.

[11]  Divya S., Panda S., Hajra S., Jeyaraj R., Paul A., Park S.H., Kim H.J., Oh T.H.

Smart data processing for energy harvesting systems using artificial intelligence

Invasive Species: Ecological Shapeshifters?

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Watershed reeds of midcoast Maine provide a deeper look into the field of epigenetics

Forests, grasslands, and marshes are ecological battlegrounds. In the fight to hold territory, maintain access to resources, and reproduce, many organisms compete directly to occupy the same niche– the role played by a specific organism in an ecosystem. An organism’s ability to carry out these roles is dictated by its “fitness” or capacity to survive and contribute its genes to the next generation. Naturally, relative reproductive success is incredibly environmentally dependent. Most organisms are tailor-made to thrive within their native habitats via natural selection. However, this biological narrative is challenged by the proliferation of invasive species in competition with their native counterparts. In their 2016 study, Spens and Douhovnikoff argue that epigenetics may be key to understanding ecological invasiveness and that the common reed (Phragmites australis) is “an ideal model species” (Spens & Douhovnikoff, 2016) for studying this rapidly expanding subfield of genetics.

Among other things, greater phenotypic plasticity, or “the ability of individual genotypes to produce different phenotypes when exposed to different environmental conditions” (Fusco & Minelli, 2010), increases an organism’s potential to adjust to its surroundings and occupy a vast variety of niches. This becomes possible through epigenetics.  Epigenetic modifications alter gene expression without changing the underlying DNA sequence (Weinhold, 2006). Methylation, the process by which methyl groups are added to DNA, is the key turning genes “on” and “off” (Menezo et al., 2020). The addition of methyl groups prevents DNA-transcribing proteins from accessing the DNA strand, stopping the gene’s expression as a protein. This has the potential to create significant differences in structural and even cellular function among individuals that are otherwise genetically identical.

Clonal plants provide a unique opportunity to study environmental pressures on epigenetics, as these individuals can act as their own genetic control. Reeds are an excellent example of this: as facultatively clonal plants, they can utilize both sexual and asexual reproduction. Exploiting this integral feature, and the existence of multiple subspecies of reed in midcoast Maine, researchers studied the genomes of both native and invasive reeds in two separate locations, Libby and Webhannet. They addressed two questions: Do introduced subspecies exhibit greater epigenetic variation (indicating that epigenetics plays a role in the success of an invasive species)? And will the variation between subspecies genotypes be lesser than the variation within a single genotype’s epigenetic markers (suggesting that epigenetic variation can be used to adapt to an incredibly variable environment)?

Researchers sought answers by studying clusters of reeds called ramets. Since all the reeds within a ramet were genetically identical, they could selectively measure epigenetic variation. These clones were grown within heterogeneous microhabitats that contain varying combinations of nutrients and conditions. Extracted DNA fragments were compared based on the level of methylation among subspecies, genotype, and ramet.

In both sites, the invasive reed demonstrated greater epigenetic diversity than the native reed (Figure 1). Up to 71% of epigenetic variation at the Webhannet site is attributed to differences among genotypes. These results suggest that clones adjust to the demands of their environments via epigenetics, rather than genotypic adaptation. Flexibility of this kind allows for rapid specialization in response to the hyper-individualized environmental conditions of each ramet. Additionally, each site developed an epigenetic “signature” with both subspecies exhibiting distinct, location specific, morphological characteristics. The significant differences in epigenetic markers between sites hint at the potential for large scale shifts due to epigenetics, should genotype not be a factor in these differences. The distinct characteristics displayed by each species demonstrate the vast alterations necessary to survive in an environment with subtle differences.

Figure 1. Epigenetic markers clustered by species (native, introduced) and location (Libby, Webhannet). Differences within a single genotype were greater than variation between genotypes, particularly for the introduced species. Figure adapted from Spens and Douhovnikoff

While this study was small scale, it supports the position that epigenotype variation provides a strong competitive advantage in the natural world. It also suggests that further study would provide more valuable information about the relevance of epigenetics in ecology. In our rapidly changing environment, due to climate change and other human influences, these native genotypes are in danger of being displaced from their niches. Despite a species’ history with its habitat, subtle alterations can have vast impact on individuals that demonstrate low plasticity or tolerance for change. Introduced organisms who demonstrate more flexible epigenotypes have the potential to outcompete their neighbors, eroding local ecosystems beyond repair. This reality drives ecological research in the direction of epigenetics, not only for the sake of discovery, but also in hopes of protecting species who cannot adapt as quickly as we disrupt.

 

Works Cited

Fusco, G., & Minelli, A. (2010). Phenotypic plasticity in development and evolution: Facts and concepts. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1540), 547–556. https://doi.org/10.1098/rstb.2009.0267

Menezo, Y., Clement, P., Clement, A., & Elder, K. (2020). Methylation: An Ineluctable Biochemical and Physiological Process Essential to the Transmission of Life. International Journal of Molecular Sciences, 21(23), 9311. https://doi.org/10.3390/ijms21239311

Spens, A., & Douhovnikoff, V. (2016). Epigenetic variation within Phragmites australis among lineages, genotypes, and ramets. Springer International Publishing. https://link.springer.com/article/10.1007/s10530-016-1223

Weinhold, B. (2006). Epigenetics: The Science of Change. Environmental Health Perspectives, 114(3), A160–A167.

Atlantic on the Brink: Climate Change Impacts to a Critical Ocean Circulation System

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Global warming due to anthropogenic greenhouse gas emissions poses an immense threat to Earth’s oceans, which serve as a vital climate regulation system. The influx of large quantities of freshwater from melting Arctic sea ice has the potential to critically alter ocean circulation in the North Atlantic. Changes to the physical properties of seawater in the North Atlantic could eventually lead to the collapse of the Atlantic Meridional Overturning Circulation (AMOC), an event that would result in potentially catastrophic changes to climate in the Northern Hemisphere. Predictive climate models have noted that this shift could occur in the future, developing a series of warnings that could help understand more accurately when this major climate shift could occur. Writing in Science Advances, Van Westen and colleagues report the findings of the Community Earth System Model (CESM) and their predictions regarding the impacts of the AMOC’s collapse.

Figure 1: A visualization of the Atlantic Meridional Overturning Circulation (Adapted from “The Ocean Conveyor – Woods Hole Oceanographic Institution,” n.d.).

The AMOC is a “tipping element” of Earth’s climate, meaning that it is very sensitive to changes in salinity and temperature and could have substantial, reverberating climate impacts if disrupted (Armstrong et al., 2022). Since 1950, oceanographic and climate data have displayed that AMOC strength has significantly decreased and is potentially in its weakest state over the past thousand years (Caesar et al., 2021). These changes largely result from an increased freshwater flux into the North Atlantic due to high rates of Arctic sea ice melting as a product of anthropogenic climate warming. This methodical increase in freshwater flux into the North Atlantic could eventually lead to the collapse of this critical ocean circulation system, an event that would have severe impacts on temperature and weather patterns, especially in the Northern Hemisphere. Prior predictive climate models, which fail to encapsulate Earth climate systems as accurately as the model used by Van Westen and colleagues, have not yet modeled an AMOC collapse. Van Westen et al.’s 2024 study is the first to definitively model this crucial climate tipping point.

Van Westen and colleagues performed their study in CESM version 1.0.5, a complex climate model that simulates earth systems (Danabasoglu et al., 2020). The research team set a preindustrial control simulation with corresponding earth and ocean system conditions at model year 0. To model sea ice melt, they added a methodical, yet variable freshwater flux from the Arctic into the North Atlantic which was increased linearly through model year 2200. This gradual increase in freshwater flux into the North Atlantic corresponded to a gradual decrease in AMOC strength, consistent with predictions made by the research team. AMOC strength began diminishing in model year 800 and abruptly collapsed in model year 1758. This collapse represented a five-fold decrease in AMOC strength over the course of a century from model years 1750 to 1850, a shockingly abrupt change given the slow, consistent freshwater flux into the North Atlantic. By model year 2000, northward heat transport by the AMOC in the Atlantic decreased to nearly zero.

Figure 2: AMOC strength at 1000m depth and 26° N latitude. Yellow band shows the range of previously observed AMOC strength (Adapted from Van Westen et al., 2024).

Researchers found influential and dynamic changes to physical properties in oceans across the globe with AMOC collapse. Sea surface temperatures (SST) in the Northern Hemisphere after AMOC collapse significantly cooled, with differences as large as 10℃ observed off the coast of western Europe. SSTs increased slightly in the Southern Hemisphere due to the near absence of northward heat transport by the AMOC. Dramatic shifts in salinity in the upper 100 meters of the ocean were observed in addition to the complete interruption of deep ocean convection in the North Atlantic. Sea-level also rose nearly 70 cm in some regions of the coastal Atlantic due to AMOC collapse.

Researchers also investigated potential effects of AMOC collapse on climate and sea-ice extent in both the Northern and Southern Hemispheres. Significant changes to Hadley Cell air circulation and the subtropical jet stream were observed. Sea ice coverage in the Arctic extended to 50°N in the Arctic (current sea ice rarely forms below 60°N), while Antarctic sea-ice retreated. Model outputs showed atmospheric temperature decreases by around 3℃ per decade in the Northern Hemisphere, a rate at which human adaptation efforts would be largely impossible (current rates of temperature increase due to anthropogenic climate warming are ~0.2℃). These temperature shifts were amplified by ice-albedo feedback, where increased ice coverage in the Northern Hemisphere after AMOC collapse reflects a larger amount of solar radiation back into space, reducing atmospheric temperatures further. Additionally, precipitation patterns in tropical regions shifted with a slight increase in atmospheric temperature in the Southern Hemisphere after AMOC tipping. These results explicitly demonstrate that AMOC tipping would have dramatic, cascading climate impacts across the globe.

Van Westen and colleagues’ study was also the first of its kind to develop a comprehensive warning system for AMOC collapse based on historical climate and oceanographic data and model predictions. Observation of freshwater transport at 34°S, an important proxy for AMOC strength, and the identification of a minimum value for freshwater transport at which AMOC collapse could occur are essential characteristics of AMOC tipping that Van Westen and colleagues identified. These markers of AMOC strength provide an observable set of characteristics that could help predict AMOC collapse in real life.

This research is especially unique because it provides a definitive, model-based answer to the question of whether AMOC collapse can occur in climate models. Prior researchers assumed that AMOC tipping was highly theoretical and would not be predicted in a model that accurately accounts for complicated elements of climate systems. Van Westen and colleagues’ findings clearly demonstrate that AMOC tipping is not only possible, but highly likely under sufficient freshwater influx due to melting Arctic ice.

While the simulation performed by Van Westen et al. (2024) represents an effective predictor of major changes in Atlantic circulation, more data is needed to optimize predictive climate models and apply findings to real climate systems. Van Westen’s research team was unable to provide a meaningful estimate of when an actual AMOC tipping event could occur due to uncertainties in the rate and effects of future climate change. In a paper examining crucial climate tipping points, another European research team estimated that AMOC collapse could occur anywhere from 15-300 years from now, with researchers agreeing that collapse may most likely occur 50 years from now (Armstrong et al., 2022). Another study by researchers from the University of Copenhagen predicted with 95% confidence that tipping may occur from 2025-2095 (Ditlevsen & Ditlevsen, 2023). Precise monitoring of the physical changes in the North Atlantic and stringent data collection are essential to develop more accurate predictions of when AMOC collapse could occur in real life.

This research by Van Westen and colleagues shows evidence that the AMOC could reach a tipping point due to freshwater transport, temperature changes, and salinity changes in the Atlantic, leading to catastrophic climate impacts across the globe, especially in the Northern Hemisphere. Predictive models of major climate events are instrumental in helping communicate the severity of anthropogenic climate change and should be utilized by scientists, policymakers, and advocates throughout the transition away from our reliance on high emission fossil fuel combustion.

References:

Armstrong McKay, D. I., Staal, A., Abrams, J. F., Winkelmann, R., Sakschewski, B., Loriani, S., Fetzer, I., Cornell, S. E., Rockström, J., & Lenton, T. M. (2022). Exceeding 1.5°C global warming could trigger multiple climate tipping points. Science, 377(6611), eabn7950. https://doi.org/10.1126/science.abn7950

Caesar, L., McCarthy, G. D., Thornalley, D. J. R., Cahill, N., & Rahmstorf, S. (2021). Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nature Geoscience, 14(3), 118–120. https://doi.org/10.1038/s41561-021-00699-z

Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., … Strand, W. G. (2020). The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001916. https://doi.org/10.1029/2019MS001916

Ditlevsen, P., & Ditlevsen, S. (2023). Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nature Communications, 14(1), 4254. https://doi.org/10.1038/s41467-023-39810-w

The Ocean Conveyor—Woods Hole Oceanographic Institution. (n.d.). https://www.whoi.edu/. Retrieved April 21, 2024, from https://www.whoi.edu/knowyourocean/oceantopics/how-the-ocean-works/ocean-circulation/the-ocean-conveyor/

Van Westen, R. M., Kliphuis, M., & Dijkstra, H. A. (2024). Physics-based early warning signal shows that AMOC is on tipping course. Science Advances, 10(6), eadk1189. https://doi.org/10.1126/sciadv.adk1189

Look to What You Know: Making Environmental Change Using What We Already Have

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Despite being conscious of the current global climate crisis, many people today feel they lack the knowledge, solutions, time, or energy to implement major environmental change. But they may be more powerful than  they think– they truly do have the power to make small-scale change in the world, if they get creative. Small groups like Glass Half Full and Swahili Modern, as well as individuals like Aviva Rahmani, use their normal daily actions and hobbies to their advantage in order to create healthy and sustainable change. 

Glass Half Full Nola was founded in 2020 by Franziska Trautmann and Max Steitz, two Tulane students who wanted to build stronger infrastructure for glass recycling in New Orleans. According to the EPA, the United States produced 12.3 million tons of glass in 2018, and 7.6 million tons of glass entered landfills. Only 3.1 million tons of glass were recycled that year (EPA). In light of this issue, Trautmann and Steitz used the resources they already had and started their project in their backyard. They hand-crushed the glass that they and their friends used in their day-to-day lives. As their community learned of their project and sent in more donations, their project expanded to a small business operating out of a glass processing facility. The company established drop-off sites and collection services all over New Orleans to increase accessibility for their new method of environmental stewardship. The donated glass gets crushed into sand and gravel for coastline restoration, disaster relief, flooring, and new glass products. With just an idea, a backyard, and some everyday tools, Trautmann and Steitz made a positive environmental impact. Though their initial plan grew into a more ambitious project, the humble beginnings of Glass Half Full Nola prove that anyone can use what they have to make meaningful small-scale change for the Earth.

Founders Franziska Trautmann and Max Steitz

 

Students in New Orleans aren’t the only ones putting their trash to good use: Swahili Modern, a fair trade company based in Portland, Oregon, distributes artisanal, handmade, African products to consumers in the United States. The business which now consists of twenty employees began with only its founder, Leslie Mittelberg. Mittelberg aimed to supply African artisans with more options for work, to give struggling artisans a stable and steady income, and to empower female artisans working from home. Swahili Modern currently distributes recycled art, and the pieces’ descriptions inform consumers of who made them and how. For example, the lion sculpture shown above was built from upcycled flip-flops. The sculptors, who work for a company called Ocean Sole, are based in a workshop in Nairobi, Kenya, and they make a living by collecting the several tons of flip-flops that wash up on the Kenyan coast each year. By working with this company, and many others, Mittelberg’s network of small businesses prove that it is possible to incorporate environmentally conscious products into a company’s regular inventory– something every small business is capable of doing.

Kenyan Artisans sculpt lion from discarded flip-flops
Artisans in Nairobi, Kenya, working against climate change and pollution

 

While artists in Africa create dazzling forms from discarded flip-flops, the artist Aviva Rahmani makes local change from right here in Maine. In her art, she embraces the idea of intersecting art and environmentalism. In 2002, Rahmani started the Blue Rocks Project to spread awareness about an obstructed causeway on Pleasant River in Vinalhaven, a town on an island in Maine. The Army Corps of Engineers had just finished construction on the causeway, leaving it narrower than before, and the construction prevented tidal flow between the saltwater and freshwater. Wetlands are vital to the health of the environment, and according to the World Wildlife Fund, the world lost about 35 percent of wetlands between 1970 and 2015 (WWF). Aviva Rahmani painted forty boulders around the causeway with complex blue designs using non toxic paint to draw attention to this serious issue. When the town subpoenaed her to wash off the rocks, she staged a “wash-in” to educate people in passing cars about the importance of maintaining healthy estuaries as she washed. The attention she brought to estuarine health helped convince the USDA to commit $500,000 to restoring twenty-six acres of vital wetlands. Rahmani wanted to make change, so she used what she had and what she knew to spread awareness for important causes. While not everyone can procure thousands of dollars from the USDA, Rahmani’s willingness to incorporate parts of her daily life into the world of environmental activism proves that anyone else can do the same.

Aviva Rahmani paints rocks with blue paint to draw attention to wetland safety.

All of these individuals and small companies making environmental change began as the rest of us are now– just people with an idea and a rudimentary set of tools to implement their plan: hammers and large containers of glass in someone’s backyard; old pieces of footwear and tools from the workshop; a bucket of paint and a rock. These simple beginnings prove to the world that anyone who wants to can make a difference in the environment. Anyone at all. On your daily walk, pick up the trash you see along the way. See how creative you can get with the soda bottles you throw away– maybe they’d make a cool plant pot. No matter what it is, the next time you have an idea that could help save the environment but don’t know where to start, just look to what you know.

 

Learn more about Glass Half Full Nola here.

Learn more about Swahili Modern’s recycled art here.

Learn more about Aviva Rahmani’s work here.

 

Works Cited

Facts and Figures about Materials, Waste, and Recycling– Glass: Material-Specific Data. EPA. Retrieved December 3, 2023, from https://www.epa.gov/facts-and-figures-about-materials-waste-and-recycling/glass-material-specific-data. 

Glass Half Full Nola— Glass recycling, coastal restoration. Glass Half Full. Retrieved October 15, 2023, from https://glasshalffull.co/. 

Kiri Technologies. (n.d.). Founders: Franziska Trautmann and Max Steitz. Kiri News. Retrieved October 15, 2023, from https://kiri.news/from-waste-to-resource-the-innovative-story-of-glass-half-full-nola/.

Our Impact. Ocean Sole. Retrieved December 3, 2023, from https://oceansole.com/pages/our-impact. 

Rahmani, A. (n.d.). Blue Sea Lavender detail on Echoes of the Islands. Aviva Rahmani. Retrieved November 10, 2023, from https://www.avivarahmani.com/endangered-species-ecoart.

Recycled Handcrafted Sculptures from Kenya. Swahili Modern. Retrieved October 15, 2023, from https://www.swahilimodern.com/collections/recycled-art. 

Swahili Modern. (n.d.). Extra Large Flip Flop Lion Sculpture. Swahili Modern. Retrieved October 15, 2023, from https://www.swahilimodern.com/collections/recycled-art/products/extra-large-flip-flop-lion-sculpture-1.

Swahili Modern. (n.d.). Kenyan artisans that build sculptures from recycled materials. Swahili Modern. Retrieved October 15, 2023, from https://www.swahilimodern.com/collections/recycled-art/products/extra-large-flip-flop-lion-sculpture-1.

Water Ecosystems Preservation — Aviva Rahmani. Aviva Rahmani. Retrieved November 10, 2023, from https://www.avivarahmani.com/water-ecosystem-preservation-ecoart. 

WWF. World’s wetlands disappearing three times faster than forests: Global Wetlands Outlook paints alarming picture of decline in world’s most valuable ecosystems. World Wildlife Fund. Retrieved December 3, 2023 from https://wwf.panda.org/wwf_news/?335575/Worlds-wetlands-disappearing-three-times-faster-than-forests.