Nature-based solutions (NbS) are neither a quick fix nor a perfect solution to tackle climate change. The sequestration of greenhouse gases through NbS takes place over long timescales relative to the rate at which humans release greenhouse gases, and the effectiveness of these solutions is sustained only for as long as they remain permanent carbon sinks. However, well-designed nature-based solutions are a cost-effective way to tackle climate change that can also deliver other social, environmental, and economic benefits. Enhancement of existing NbS and the creation of new ones is likely to play an important role in any long-term sustainable climate change strategy, working alongside engineered approaches. 

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What Are Nature-Based Solutions?

Nature-based Solutions began to be discussed in scientific papers in the early 2000s and represented a shift in attitude by researchers, policy makers and activists in their approach to tackling climate change. These solutions aim to protect and develop naturally-occurring ecosystems to help meet reduced emission targets and environmental goals. A key element of the nature-based solutions approach is enhanced carbon sequestration – the capture and storage of greenhouse gases. If countries are to achieve net-zero emissions, carbon dioxide, methane and nitrous oxide levels in the atmosphere must be stabilised and reduced. It is essential that these compounds remain stored, perhaps in plants or the oceans, and that the natural environment can continue to absorb greenhouse gasses.

Nature-based solutions sit alongside hard engineering approaches like carbon capture and storage as a way to achieve net-zero emissions. In particular, NbS could reduce pressure and reliance on strategies such as bio-energy with carbon capture and storage (BECCS), which is costly and limited by the amount of biomass that can be farmed. Other beneficial outcomes of NbS can include reducing flood risk, protecting biodiversity and improving air and water quality.

Despite the relatively recent use of the term in modern-day science, NbS have been utilised by Indigenous People and Local Communities (IPLCs) across the world for centuries to protect their local environments. Despite comprising less than 5% of the world’s population, IPLCs protect over 80% of biodiversity in areas that store at least 24% of the total carbon in global tropical forests. As explained in the report “Cornered by Protected Areas,” progress means adopting rights-based approaches to conservation that bring justice for IPLCs, whilst enabling biodiversity conservation and climate action.

3 Types of Nature-Based Solutions

1. Protecting and expanding existing natural ecosystems

Examples: Peatland rewetting, ceasing deforestation.

2. Developing sustainable procedures for managing or restoring ecosystems

Examples: Agroforestry, reforestation and blue carbon initiatives

3. Creating new ecosystems that can sequestrate greenhouse gases

Examples: Establishing green buildings (includes green roofs and walls)

Each approach has different benefits to local communities and ecosystems and varying execution and upkeep costs.

Protection and Restoration of Peatlands and Wetlands

Peatlands are important ecosystems for regulating greenhouse gases in the atmosphere as undrained peatlands form large carbon sinks as a result of peat accumulation. Around 15% of the world’s peatlands, covering less than 0.4% of the global land surface, have been drained. Drainage of peatland emits huge amounts of CO2 into the atmosphere, so it is imperative we protect and restore these environments sustainably. This can be done through rewetting, raising the water table of peat bogs to increase greenhouse gas storage. Over time this greenhouse gas exchange can become close to that of a natural, undrained peatland. The solution has also been found to have a climate-cooling effect across various climate and land-use categories. Rewetting is a much more effective strategy than peatland initiation which can take much longer and still not store equivalent amounts of carbon and nitrogen as older peatlands.

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Forestry Solutions

Deforestation and forest degradation releases an estimated 4.4Gt of CO2 every year (equivalent to 10% of annual global CO2 emissions), so preserving existing forest is a quick win.

Reforestation is the most familiar and recognisable NbS; trees absorb and sequestrate carbon dioxide. However, the solution is more complex, and reforestation should only be part of a wider strategy, particularly if short-cuts are taken which risk creating monocultures and introducing invasive species into local ecosystems. It has been estimated that by restoring 350 million hectares of degraded forest landscapes  by 2030, an area twice the size of Alaska, 1-3 Gt of CO2 could be sequestered per year. However there are obvious logistical barriers to this which have been debated in scientific articles. Despite this, the solution is highly cost-effective and therefore easier to promote amongst private stakeholders and governments concerned with short-term returns. 

A lot of agricultural land across the world is already degraded, with climate change expected to exacerbate the loss of soil-organic carbon (SOC) through processes such as decomposition, mineralisation and erosion. To minimise these emissions, SOC stocks need to be protected whilst carbon saturation levels are increased. Methods include agroforestry – the planting of trees amongst crops which allows farmers to maintain yield sizes in cases of climate variability. This method also reduces soil erosion and limits the loss of soil organic carbon whilst increasing the ability of agricultural land to sequester greenhouse gases.

Successful forestry initiatives reward farmers and landowners for providing environmental benefits. A frequently cited success story is the Payment for Ecosystem Services (PES) scheme in Costa Rica. Christina Figueres, the former Executive Secretary of the UNFCCC, has commended using taxes as a way to fund better agricultural practices. PES is a strategy that was adopted by Costa Rica in 1996 to confront its huge deforestation problem. It is estimated that the strategy prevented 11 million tonnes of carbon emissions being released between 1999 and 2005, with Costa Rica’s forest cover actually increasing over the last 20 years despite continued, managed deforestation. There are still improvements to be made, as Costa Rica PES is limited by a flat-rate payment scheme that benefits farmers with smaller land parcels, restricting larger-scale implementation. Most PES plans are funded by national governments and involve intermediaries, usually non-governmental organisations (NGOs), proving that communication and cooperation between governments, NGOs and private stakeholders is crucial if a system is to be implemented and sustained successfully.

Blue Carbon

71% of the Earth’s surface is water. So called ‘blue carbon’ includes carbon captured and stored in vegetated coastal ecosystems, specifically mangrove forests, seagrass beds and salt marshes. Since the 1980s, between 20-30% of anthropogenically produced CO2 emissions have been captured in oceans, contributing to ocean acidification. The key benefit of blue carbon storage is their ability to increase their carbon capacity over time, unlike forests which can reach a saturation point and can only store carbon for decades or centuries at most. There is also a lot of ongoing research into the nature of other marine NbS, which may reduce the strain of ocean-acidification on vulnerable ecosystems such as coral reefs. Kelp forests are particularly interesting examples of NbS as the nature of the plant, a macroalgae, means it can float for long distances out to sea and therefore its carbon content can be sequestered in deep ocean stores. However, these potential solutions need more research and investment and are unlikely to assist materially in achieving 2030 and 2050 emission targets unless progress is significantly accelerated.

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Climate Mitigation Potential

The 2014 Climate Change Synthesis Report published by the IPCC states that “stabilising temperature increase to below 2°C relative to pre-industrial levels will require an urgent and fundamental departure from business as usual.” Nature-based solutions are a key component to achieve net-zero emissions, and crucially are more beneficial and acceptable to communities that want to protect and restore their natural environment without hard engineering methods. Cost‐effective nature-based solutions can provide around 30% of immediate climate mitigation needs through protection, management, and restoration of ecosystems. However, the true mitigation potential is still unknown as environmental-based approaches have disproportionately struggled from lack of investment and research. 

Nature-based solutions also work best over long timescales; they are no ‘quick fix’ to the net-zero goal as they require huge amounts of land, water resources and time to reach their carbon saturation and mitigation potential. For example, whilst older trees have larger stores of carbon the ability of forests to sequester carbon decreases with age, and some of the carbon may be rapidly released by wildfires (which in turn rejuvenates the forest).

Cost-Effectiveness

The implementation and maintenance costs of NbS are often offset by the benefits, for example disaster risk management along river catchments and in coastal regions. Natural flood management is a pressing concern in places like the UK, believed to be exacerbated by the impacts of climate change. Solutions such as regenerating natural woodland within a river catchment and constructing leaky dams have been found to reduce the hazards of flooding on smaller catchments, although not for more extreme events.

Nature-based solutions are seen as multi-functional solutions that can manage carbon dioxide sequestration whilst also improving water security, food security and human health, however this holistic approach can make  cost-analysis models more complicated due to the difficulty of attributing economic value to these benefits. Recent advancements in environmental modelling have helped reduce uncertainties and illustrate the varying levels of protection different NbS can offer, for example, reducing the frequency and intensity of natural hazards like flooding.

More research needs to be conducted to ensure planning applications address concerns about balancing present costs with future benefits. Part of the appeal of engineered solutions is their fixed costs, short timescales and relatively certain outcomes. There is widespread consensus among policy makers, ecologists, engineers and geomorphologists that a combination of nature-based solutions and engineered solutions is often optimal to protect a specific environment. For example constructing vegetated levees and managing wetlands outside New York to protect against storm surges like those produced by Hurricane Sandy in 2012. 

Challenges of the Nature-Based Solutions Approach to Tackle Climate Change

A huge challenge to implementing NbS is water availability, which these strategies invariably require in huge quantities. Water scarcity is already a significant problem in many regions, for example the Middle East and North Africa.  Another limiting factor is the lack of available space, as with an ever-growing global population, productive land is in increasingly short supply. Therefore multi-functional landscapes that sequester carbon whilst delivering other valuable benefits such as food production, preservation of biodiversity and risk mitigation are preferred for NbS interventions. However, these benefits may vary in both their spatial and temporal distribution. Therefore coordinated action between stakeholders, the public and policy makers is required, with policy interventions designed to ensure decisions are rewarded (or penalised) by the benefits (or costs) that accrue to all stakeholders, not just to decision-makers. Ultimately, NbS can only work in conjunction with other strategies if they are implemented sooner rather than later, and in a way that respects and protects the ecosystems and communities that are to be altered.

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There has been much discussion and debate in the scientific community regarding the efficacy and suitability of machine learning techniques to help improve our understanding of local and global environments. Machine learning allows for predictive and probability-based calculations to be undertaken – which are useful tools for evaluating the benefits and costs of our actions in the present. It is useful for those active in climate science to understand the strengths and limitations of current machine learning techniques, as this results in better understanding and criticism of any published findings and conclusions. 

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What is Machine Learning? 

Machine learning falls under the broader term Artificial Intelligence (AI), which is defined in a 2004 paper as “the science and engineering of making intelligent machines, in particular intelligent computer programs”. The true nature of ‘intelligence’ is hotly debated, but for this purpose, intelligence is artificial, in the sense that computer models are used to draw conclusions from complex datasets. Models are usually designed for research that would be impractical or excessively laborious to carry out with conventional analysis. 

The diagram below illustrates how popular machine learning terms are related:

It is also important to understand the following five terms: 

• An algorithm is a set of instructions (in this context, supplied to a computer) that transforms input information into output information. For example, calculating the carbon footprint of an organisation by assessing variables such as fuel or energy consumption, manufacturing processes, and any offset efforts. 
• A model is the algorithmic representation of a system (such as climate or an economy). Usually, a model comprises multiple algorithms that solve a complex problem. 
• Structured Data is data that is labelled, where its nature has already been determined, for example, temperature values. Classical machine learning mainly uses structured data. 
• Unstructured Data is data presented in raw forms, such as images. Deep learning models can operate on both structured and unstructured data to create natural language processing and visual recognition systems. However, these require higher levels of computing power than classical machine learning methods. 
• Neural networks are one of the most important computational techniques for machine learning. A neural network is a software model consisting of several connected nodes. Both the nodes and the connections are important. Below is a simple diagram of how neural networks can be structured.

Each network has inputs from either data or previous nodes, one or more hidden layers (algorithms that can modify the input), and an output. If a node’s algorithm produces a result that exceeds a set threshold value, then the output is activated. Each connection can also be assigned a weight to indicate how useful it is in predicting an overall result. Connections that are more useful in predicting a result receive a higher weight. Less useful connections are assigned a lower weight or may even be dropped. 

Therefore, with the repeated presentation of data and comparison of the predicted outputs, the neural network learns to represent the system being modelled more accurately. If there is confidence in the model,  it can be applied to new datasets, where answers are not understood, or to hypothetical datasets that might exist in the future.  

Machine learning methodologies can also be categorised into three types of learning: Supervised, Unsupervised, and  Reinforcement learning. These are summarised below and are selected based on the type of data being used and the desired output:

• Supervised learning is appropriate when the data are considered well understood (usually structured datasets) but the relationship between them is complex – for example, economic modelling.  
• Unsupervised learning is used to create insights from unstructured data not directly related to the problem being solved, such as imagery or sound.  
• Reinforcement learning is used to optimise algorithms based on trial and error, again through the repeated presentation of data. The algorithms that most successfully predict the correct answer are preferred. 

What are the Data Requirements for Machine Learning Techniques? 

The data requirements for different machine learning techniques vary based on the requirements of each model. Usually, the dataset needs to be large enough to be split into training and testing subsets. The training dataset is used to train the model, whilst the testing dataset is used to assess the accuracy of the model. Some research, however, also requires an additional validation dataset. Typically, data are split in a 70/30 ratio between the training and testing sets and this can be done in a variety of ways. It can be split spatially (for example by region), temporally  (over different time periods), or even categorised based on different variables, for example land cover. 

3 Ways in Which Machine Learning Techniques Help Address Climate Change

1. Improved Data Analysis 

Machine learning can help tackle climate change by looking at data to spot patterns and trends that are not recognisable to the human eye or are not practical for humans to monitor. For example, machine learning models enable automatic and continuous monitoring of global imagery to identify wildfires, landslides, and other visible phenomena using pattern and image recognition. Reinforcement learning allows the models to become increasingly accurate in identifying changes and hazards. These can then be identified and evaluated by an expert and forwarded to the relevant authority for mitigation. 

Other applications combine disparate datasets to draw new conclusions or enable the identification of useful insights. For example, deforestation or coral bleaching data could be combined with meteorological data to understand how each impact the other. 

A more abstract application is sentiment and preparedness analysis. This seeks to understand human thoughts and feelings towards climate change and attitudes to mitigation efforts. The data are usually collected through social media or crowdsourcing strategies. 

By assessing the collective feelings and attitudes of communities towards tackling climate change, organisations and authorities can help improve services, for example, hazard preparedness schemes or local initiatives to help improve quality of life. By comparing the attitude of different demographics, it is possible to identify targets for information, education, and strategies to combat disinformation. 

2. Optimising Systems and Solutions 

Machine learning can tackle climate change by enhancing or adjusting technical systems to best utilise resources, based on contextual information supplied to the model. For example, automated electricity grids optimise energy production by monitoring and predicting energy supply and demand. Machine learning could use traffic information to predict demand for electric cars charging the following night. This can also be applied to local initiatives, for example trying to reduce the urban island heat effect by using machine learning to optimise urban planning, considering variables such as infrastructure and vegetation cover. 

Another example is carbon sequestration modelling. This technique assesses how much carbon is being stored in different forms across the globe. Machine learning models can be used to simulate carbon sequestration and its impact over time – which can then be used to design smarter carbon capture systems.  

3. Scenario Modelling and Planning 

A third use of machine learning to tackle climate change is the prediction and modelling of future scenarios under anthropogenically induced climate change. One of the most urgent applications of this is modelling the frequency and severity of extreme weather events. This can include droughts, wildfires, extreme precipitation, flooding, and landslides. This task can be accomplished by associating variables (for example, temperature and precipitation) with the occurrence of a specific hazard, to then predict how the frequency or severity of that hazard may change under various future scenarios. Predictive modelling can also be used to monitor the impacts of different scenarios on ecosystems, both in terms of species population modelling and also to address how long-term processes such as the rate of coral bleaching may vary under different environmental conditions. 

What are the Benefits of Using Machine Learning to Tackle Climate Change? 

The key benefit of utilising machine learning is that it allows us to simplify, categorise and make predictions based on highly complex datasets. Data can be analysed across larger spatial and temporal scales to make observations on intricate processes, allowing for global monitoring and mobilisation. In terms of future development, machine learning is becoming an increasingly viable technique for data analysis as the cost of processing power and data storage reduces, driven by the efficiencies of cloud computing. Furthermore, a huge increase in data availability,  fuelled by different resources such as the Internet of Things and crowdsourcing methods allows for the expanded application of machine learning techniques to tackle climate change. 

What are the Limitations and Risks of Machine Learning? 

Four limitations of machine learning must be recognised to ensure the integrity of model outputs:

1. Lack of Data 

Machine learning works best when the models are trained on a wide range of scenarios, in which the full impact of each variable can be evaluated; including extreme and edge cases. Given that high-quality satellite data has only been available for less than sixty years, environmental machine learning models are largely limited to recent decades. There are no datasets from major ice ages or interglacial periods from which to learn how the environment might change under more extreme conditions. There is, therefore, a risk that machine learning models will fail to identify relationships and feedback loops for situations outside those upon which it has been trained. 

This makes a lot of machine learning techniques with current datasets unsuitable for long-term predictions. When looking at long-term environmental changes, change must be considered over centuries – even if the media focuses on the immediate concerns of the next 50-75 years. There are other contexts where the datasets are simply unavailable. For example, as of 2022, less than 25% of ocean floors have been mapped globally. Once achieved,  completed mapping could improve the management of fisheries and conservation efforts.

2. Errors, Bias and Incomplete Data 

Whilst data collection methods are becoming increasingly accurate and reproducible due to increased automation and higher quality measuring instruments, there is still an abundance of errors that can occur. If these are not addressed, they can invalidate the conclusions drawn by machine learning models. Whilst this can be mitigated by making comparisons to previously collected datasets or long-term averages, the risk of error is not completely avoidable. 

Another matter is the risk of inherent bias in the data collection method, whether this is through the choice of data used to train the model or the context in which the data was collected. Put simply, machines can only learn from the data supplied; any factors outside those datasets will not feature in any learned model. Whilst this is not always a  problem, it is important that this is included in any commentary on the data. An example of this is mentioning the impact of the Covid-19 pandemic on the drop in global carbon dioxide emissions during 2020.

3. Comprehensibility 

Machine learning can produce highly accurate models which can adapt to different data inputs and scenarios, but may not yield formulae or relationships that are visible for independent verification. A concern often raised by scientists is the risk of relying on ‘black box’ machine learning models that humans cannot understand. 

Neural networks are a self-adapting, data-driven abstraction of reality; the inputs and the outputs can be observed but there may be no inherent logic within the neural network that can be critically reviewed – it is effectively accumulated experience acquired by repeated observation. Considering this, can the model be trusted to be accurate in the future just because it has been accurate in the past? Of course, this criticism can be levied at many scientific theories that are based on analysis rather than derived from fundamental principles, but such theories generally include within them a comprehensible logic to which some level of certainty or risk can be assigned. 

As a result, it is becoming an increasingly common requirement that machine learning models are interpretable. This results in a more ethical but constrained model based on a limited number of variations. Understanding both the benefits and limitations of black box models allow us to compare and critique different machine learning algorithms in a proactive way.

4. Energy Consumption 

A final consideration of using machine learning techniques to tackle climate change is whether the findings negate the carbon emissions produced by the storage and analysis of such large datasets. As data storage and computing power become increasingly optimised and renewable power availability increases, this should become less of a  concern. 

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Will Machine Learning Help Tackle Climate Change? 

The consensus amongst climate and earth scientists is that machine learning models are powerful tools. Used wisely,  machine learning has the potential to make climate science more widely available and applicable through the industrialised analysis of data. Furthermore, since machines have no inherent bias, deep learning in particular, may  produce insights that elude other forms of research, either because the data was not suitable for traditional analysis or because the inference is unexpected.  

However, the majority of black-box models are not ideal for providing us with reliable projections outside the range of data used to train the model. Models can only learn to generate outputs within the range of the inputs used for training and therefore, they often have no knowledge of errors or extraneous factors that might also be relevant.  Therefore, it must be accepted that researchers often cannot articulate exactly how neural networks come to their conclusions, which makes it risky to rely on them alone for critical decisions.

The resulting push for greater transparency and availability has led to an increased publication of machine learning model code scripts, in addition to the datasets used. As technological capabilities develop, it is essential that climate change models are based on the scientific processes that underly the Earth’s natural systems and cycles. These large-scale models could incorporate machine learning algorithms, however most likely as part of a larger solution. 

In a commercial context, one up-and-coming platform for tackling climate change using machine learning is  Microsoft’s AI for Earth Programme. Launched in 2017, it aims to distribute two hundred research grants (totalling $50 million) to projects using artificial intelligence to address environmental damage. Using Microsoft’s platform and interface, researchers and scientists can share data, methods, and conclusions, directly allowing for increased transparency and critical analysis. The goal is to create a collaborative space to mitigate climate change impacts by connecting experts. Other initiatives include Climate Change AI and the Climate Science for Service Partnership China, both of which are collaborative science initiatives between research institutions. 

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The post Can Machine Learning Help Tackle Climate Change? appeared first on Earth.Org.

The environmental footprint of the online world is constantly expanding as its energy consumption rises to meet demand, but there are benefits too, which must be set against the costs. Greenpeace estimates that by 2025, the technology sector could consume 20% of the world’s total electricity; this increase from 7% currently is attributed to the expansion of cloud computing and the further development of new technologies, such as artificial intelligence, which require a great deal of computing power. Despite these claims, many recent reports by companies such as Microsoft have stated that the growth of cloud storage has had a minimal impact on energy consumption and further improvements in efficiency will negate the impact of ever-expanding storage and processing. What is the truth of the environmental impact of cloud computing?

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What is Cloud Computing?

Cloud computing, despite existing since the early 1960s, was developed as a marketing term in the late 1990s. The idea is that data can be collected, analysed and stored in specialised, shared data centres all over the world before being accessed through a multitude of web-enabled services. The data can be processed online, and is fast becoming the go-to solution for commercial and government systems, with cloud applications ranging from personal productivity tools like Office365 to large database solutions powered by Microsoft Azure.

In 2018 it was estimated that 3.6 billion people were accessing a huge range of cloud computing services including Google Drive, Office365, Oracle, Netflix and Dropbox. The largest providers of cloud computing are Amazon Web services (AWS), Microsoft Azure and Google Cloud Platform.

Electricity Demands

The main impact of cloud computing is the vast amounts of electricity required to power the servers and keep them cool. In 2012, the IT sector consumed more electricity than the entirety of Russia and just under half the total consumption of the US. Demand for cloud computing will only continue to grow; an IDC Study claims that by 2025, worldwide annual data traffic will increase by 60% to 175 Zettabytes (175 trillion Gigabytes), with cloud computing applications driving the majority of this growth.

So is cloud computing bad for the environment? To fully understand the overall environmental impact of cloud storage we must compare it to the alternative – local computing.

Environmental Benefits of Cloud Computing

Cloud computing is more efficient and resilient than local computing capacity for individuals and firms, and also offers geographic redundancy, making data loss unlikely even in the event of a natural disaster. A 2013 research paper, funded by Google, revealed that by moving commonly used software applications to the cloud, energy usage would decrease by 87%. Furthermore, cloud computing is a major enabler of both home and remote working, reducing the need for commuting and therefore decreasing emissions. 

Large-scale data centres are also more likely to cost-effectively recover and reuse heat. Google reported that whilst the amount of computer processing at its data centres increased by 550% between 2010 and 2018, the amount of energy consumed grew by just 6%. If demand growth and efficiency savings are broadly in balance then it may be possible to deliver the projected growth in data centre capacity without significantly increasing energy requirements.

Cloud Computing Pollution

Computer equipment generates a lot of heat and so data centres must be kept cool. Cooling typically accounts for 40% of total energy consumption and up to 80% if the natural climate of the data centre is warmer. The ACDC (Arctic Circle Data Centre) has adopted a novel approach; located in the closest Norwegian town to the Arctic Circle and powered by hydroelectricity, the centre is planning to open in 2021. The US-Norwegian partnership, called Kolos, claims the local climate and access to hydro power will cut energy costs by as much as 60%. However, moving data centres towards the poles is not a straightforward venture; building upon recent concerns about data security and encryption, many countries and trade areas such as the EU have passed laws that require citizen data to be stored on servers within their national borders.

Furthermore, an additional environmental impact of cloud computing is the electronic waste produced by the industry. In 2018, 50 million metric tons of e-waste was generated globally as, for commercial reasons, equipment is often replaced as soon as more efficient technology becomes available. Other environmental impacts of data storage include the coolant chemicals used in the server rooms, which are often hazardous, and the battery back-ups of the data centres. The components of these batteries are often mined unsustainably, and the disposal of both toxic batteries and the chenvemical coolants could have a devastating impact on the local environment if not properly managed.

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Cloud Computing Sustainability Opportunities

Cloud computing is dominated by a few large corporations, making it more likely that public pressure can be effective in reducing their environmental impact. The three largest cloud computing providers are all amongst the world’s top five most valuable companies. Amazon, Google and Microsoft in particular have been subject to both praise and criticism in response to their sustainability and environmental practices. Google has been carbon neutral since 2007 through their use of renewable energy and carbon offsetting purchases; the company has offset over 19 million metric tons of carbon dioxide equivalent (tCO2e) in the last 12 years. Whilst there has been much debate on the sustainability and effectiveness of carbon offsetting as a long-term strategy, there is no doubt this is a positive change for the IT industry in response to growing emissions.

In 2018, Amazon exceeded 50% renewable energy usage for the year and have since announced and begun construction of four new wind farms in Ireland, Sweden and the US. These wind farms, combined with their 9 pre-existing renewable energy projects, should generate the same amount of electricity as the annual consumption of over 260 000 US homes. However, in February 2019, a Greenpeace report accused Amazon of abandoning its commitment to 100% renewable energy, noting that some of Amazon’s largest data centres in Virginia are powered by just 12% renewable energy. Amazon responded to the report by reaffirming their commitment to 100% renewable energy and have extended this by pledging to net-zero carbon by 2040. At the time of publication, they have yet to publish a realistic plan for meeting this goal.

Risks

Despite the ongoing initiatives, cloud computing is not yet at net-zero carbon and therefore the environmental cost must be compared to its benefits. Some larger scale uses of cloud computing, such as mining crypto-currencies, arguably have little societal value and it has been reported that the Bitcoin industry alone could produce enough CO2 to result in a 2 degrees Celsius warming by 2050.

The upcoming decade will see continued growth in demand for creating effective mass data storage and processing capacity. It is unclear whether demand growth will exceed improvements in energy efficiency, and thus whether the energy footprint of data centres will grow or stabilise. This is why it is critical that large corporations invest in next-generation storage and cooling technologies to cope with the expected growth in demand. Ideally this will run alongside continuing investments in renewable power, minimising the environmental impact of emissions from this sector as it expands.

Ultimately, cloud computing is more energy efficient than the alternative and facilitates environmentally beneficial services and economic growth. However, it is not without environmental ramifications and therefore consumers should demand the highest environmental standards alongside future plans for green investments. As we look into the future, the only safe, sustainable way to ensure minimal environmental impact alongside the development of cloud computing is to vocalise and establish these requirements. If we demand greater transparency and improved global standards, we can have it all.

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The post Uncovering the Environmental Impact of Cloud Computing appeared first on Earth.Org.

The climate crisis and invasive species are often studied as two independent threats to global biodiversity. However, studying the relationships and feedback loops between them can create a more complete analysis of how Earth’s natural systems are being changed and shaped by anthropogenic forces. An invasive species is defined as a population of species that have become established, permanent residents, in an environment that is not their native one, causing ecological and economic harm. Species very rarely ‘invade’ by themselves and are usually introduced by humans, often by accident. Despite only around 10% of introduced species becoming invasive species, the total cost in the US as reported in 2005 by ecologist David Pimentel is nearly $120 billion USD a year.

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Why are Invasive Species Becoming More Common?

Since the mid-1800s world trade has grown over 140-fold. This increase in aerial transport and container shipping has increased the amount of biological matter getting transported around the globe. This is coupled by an overarching trend of poleward migration and emigration by multiple species, fuelled by an increase in surface water temperatures that are currently climbing by 0.18 °C per decade. Even if warming is not further exacerbated, that still suggests an increase of over half a degree by 2050.

Why are Invasive Species a Problem?

The impacts of invasive species are more widespread than just economic damage. One of the most studied cases of a singular invasive species and its impacts is the brown tree snake (Boiga irregularis) in Guam. It was most likely introduced by military traffic in the 1950s following World War II. By 1968 it was widespread across the island and has since resulted in the extinction of 10 native forest bird species. The consequential impacts of this extinction have been cascading ecological effects affecting native invertebrates and pollinators and has led to a decline in native plant species. From a social perspective the fear of snake bites must be considered alongside the loss of productivity resulting from multiple power-outages as snakes interrupt power lines. 

What is the Situation in Guam Now?

As the situation in Guam unfolds this example can tell us a lot about how an invasive species might be impacted by the climate crisis in the future. Currently the brown tree snake population on the island is declining. This is suspected to be the result of the population of Brown tree snakes on Guam exceeding the carrying capacity (the maximum population size of the species that the environment can sustain indefinitely), meaning the population is not stable. In addition to this, a study published in the Biological Conservation Journal in 2005 suggested that the stress of overcrowding and competition for food resources limits reproductive ability. Even with this developed understanding, it is still unclear whether this will result in the stabilisation of the population or if it could create an opportunity for Guam’s naturally existing predator populations to recover, even temporarily. From an economic perspective, the annual costs of Guam’s brown tree snake research and management efforts are USD$7 million. 

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The Threat to Our Oceans

This is not only a threat affecting terrestrial biodiversity. Although increases in global mobility have increased the likelihood of introduced species becoming established on land, the link between the climate crisis and an increase in the number of invasive species might be most apparent in aquatic environments. Warming oceans allow species to create corridors across water bodies that may previously have been too cold for them to survive in. If the ecosystem they inhabit is vulnerable to a new predator and the majority of species are specialists (species that have evolved and adapted to be suited to a certain environmental niche), then it is likely to have large ecological consequences. For example, in recent years there has been a pronounced expansion of invasive thermophilic species to the central and western basins of the Mediterannean Ocean. This expansion has been intensified since the 1800s by the construction of canal pathways, in this case the Suez Canal.

Future of Invasive Species 

The uncertainties of the climate crisis leave a lot of questions about the future of invasive species and whether ‘invasive hotspots’ identified in studies as areas vulnerable to increased numbers of invasive species will be realised. It is also possible that a warmer climate might eradicate some invasive species, particularly in isolated locations where they might be unable to migrate to more favourable climates.

As species migration surges as a result of the climate crisis, questions have been raised concerning the classification of invasive species. If a native species population migrates or emigrates further poleward to higher altitudes where they used to be absent, is this considered an invasive species? The consensus by scientists at this stage is that they will still be considered native unless they cause discernible damage to their environment, whether through seasonal migration patterns or by displacing the pre-existing species that occupied a specific ecological niche.

The post Invasive Species: How Concerned Should We Be? appeared first on Earth.Org.