Using Remote Sensing to Analyse Net Land-Use Change from Conflicting Sustainability Policies
In order to achieve the ambitious Sustainable Development Goal #11 (Sustainable Cities and Communities), an integrative approach is necessary. Complex outcomes such as sustainable cities are the product of a range of policies and drivers that are sometimes at odds with each other. Yet, traditional policy assessments often focus on specific ambitions such as housing, green spaces, etc., and are blind to the consequences of policy interactions. This research proposes the use of remote sensing technologies to monitor and analyse the resultant effects of opposing urban policies. In particular, we will look at the conflicting policy goals in Amsterdam between the policy to densify, on the one hand, and, on the other hand, goals of protecting and improving urban green space.
We conducted an analysis to detect changes in land-uses within the urban core of Amsterdam, using satellite images from 2003 and 2016. The results indeed show a decrease of green space and an increase in the built-up environment. In addition, we reveal strong fragmentation of green space, indicating that green space is increasingly available in smaller patches. These results illustrate that the urban green space policies of the municipality appear insufficient to mitigate the negative outcomes of the city’s densification on urban green space. Additionally, we demonstrate how remote sensing can be a valuable instrument in investigating the net consequences of policies and urban developments that would be difficult to monitor through traditional policy assessments.
Source: Giezen M, Balikci S, Arundel R. Using Remote Sensing to Analyse Net Land-Use Change from Conflicting Sustainability Policies: The Case of Amsterdam. ISPRS International Journal of Geo-Information. 2018; 7(9):381. https://doi.org/10.3390/ijgi7090381
Nederlandse provincies experimenteren volop met de inzet van data, algoritmen en nieuwe technologie. Zo worden bijvoorbeeld populaties zoogdieren gemonitord met live cams en automatische beeldherkenning. Digitalisering levert op verschillende vlakken nieuwe kennis op en stelt provincies in staat om burgers en maatschappelijke organisaties nauwer te betrekken bij het beleid op voor hen belangrijke thema’s.
Data en technologie digitaliseren de leefomgeving. Sensoren en digitale zenders monitoren verkeer en wijzen slimme auto’s de weg. In de natuur staan camera’s die automatisch dieren kunnen tellen. En burgers doen mee met het meten van luchtkwaliteit. Dat biedt nieuwe inzichten voor beleid, maar roept ook tal van maatschappelijke, ethische en politieke vragen op.
In dit essay verkennen we, op verzoek van Gedeputeerde Staten van de provincie Noord-Holland, deze vragen. Dit doen we voor zes kernopgaven: biodiversiteit, mobiliteit, lucht, water- en bodemkwaliteit, economische transitie, energietransitie en verstedelijking (‘wonen en werken’), op basis van desk research en zes interviews met vertegenwoordigers van de provincie Noord-Holland, Zuid-Holland en Zeeland.
The next 5 years could prove to be a global turning point for privacy and personal data protection. Most of the world will have ageneral data protection law, including the largest countries currently without one –India, Indonesia and, quite possibly, the United States. Most policy interventions addressing social, environmental and public health issues, will involve technology and data usage. Data protection will become relevant in almost every context. The Covid-19 crisis, which, initially, seemed to be a danger to such an evolution, has, instead, strengthened the call for the protection of individuals’ privacy. This is especially the case when governments take measures to defend society and the economy against such an extraordinary threat.
The various economic and social implications of “digitalisation” have been discussed in many scientific disciplines and regarding manifold aspects. For instance, early analyses on the digital economy began with Tapscott (1994) and Rochet and Tirole (2003), while publications on digital capitalism date from Schiller (1999) to Staab (2019). Yet, ecological economy research has only marginally touched upon the issue of digitalisation so far. Despite a surge in publications regarding Green IT already in the 2000s and attempts to research ICT for Sustainability from a comprehensive and interdisciplinary perspective more recently, a particular focus on challenges related to governing economic activities linked to digitalisation in a way that these promote sustainability, is still emerging.
Artificial Intelligence for Environmental and Climate Protection
There are numerous ways in which applications powered by intelligent algorithms can be used to benefit the environment and protect the climate. But to ensure an overall positive impact on the environment, Artificial Intelligence applications should be used with caution, and most importantly, only promoted in areas where they really make sense.
Artificial Intelligence (AI) has long since found its place in our everyday lifes, in companies and in industry – whether it is as a search engine, a personal voice assistant or robots and autonomous machines programmed for specific activities. However, projects, start-ups, companies, and research projects that are developing and testing the use of AI to protect the environment or the climate are still an exception: A keyword search on Crunchbase by reset.org in June 2020 for the DBUfunded publication Greenbook (1): Künstliche Intelligenz – Können wir mit Rechenleistung unseren Planeten retten? [Artificial Intelligence – Can Computing Power Save Our Planet?] (RESET 2020) revealed around 400 AI start-ups with a sustainability focus – compared to a total of almost 20,000 AI start-ups worldwide. A similar picture emerges in research. Even though there is an increasing number of studies that focuses on individual areas of application of AI in the context of sustainability, so far there are no studies at either a European or an international level that enable us to thoroughly evaluate research activities in this field. A short study commissioned by the German Environment Agency comes to the same conclusion (UBA 2019). When looking at specific AI applications, it becomes clear that there is a large range of different areas where it is possible to apply AI to protect the environment and the climate, and that intelligent algorithms are in fact already proving to be highly effective (RESET 2020).
Sustainability challenges of Artificial Intelligence and Policy Implications
Automated decision-making based on Artificial Intelligence is associated with growing expectations and is to contribute to sustainable development goals. Which opportunities and risks for the environment, economy and society are associated with Artificial Intelligence-based applications and how can they be governed?
Advances in Artificial Intelligence (AI) effectiveness have made its application ubiquitous in many economic sectors. Whether speech or facial recognition, computer games or social bots, medical diagnostics or predictive maintenance, or autonomous driving, many actors expect opportunities not only for product innovations and new markets but also for new research perspectives. Economic and political actors alike expect AI-based systems and applications to contribute positively to sustainability goals (Jetzke et al. 2019). These include, for example, the opportunities offered by AI for improving the management of smart grids (Jungblut this issue), and transport infrastructures, for conducting more precise earth observation, for creating new weather warning and forecasting systems, or for enhancing solutions for waste and resource management.
A Marriage Story of Digitalisation and Sustainability?
Can digitalisation be part of the solution to pressing sustainability challenges? Or are current developments going to impede a socio-ecological transformation? The answer is not black and white; it is complex and cross-cutting. We analyse key problems and give an outlook on possible solutions.
The United Nations Sustainable Development Goals (SDG) provide a guiding framework for worldwide policies that ensure a good life for present and future generations. If the SDG are to be met, resource consumption, greenhouse gas emissions, poverty and inequality have to be reduced as far as possible, while education, welfare, climate protection, and biodiversity should be promoted to expand and flourish in future years. Digitalisation, here understood as the permeation of various information and communications technology (ICT) devices and applications (hard- and software) into diverse areas of everyday life, society, and economy, may have significant implications on how the SDG can be achieved.
On the positive side, digital tools and applications may serve as levers and can trigger dynamic sustainability transformations in various sectors. For instance, several reports outline the potentials of digitalisation to increase energy efficiency, avoid resource waste, improve access to sustainable services, and innovate new sustainable practices (e.g. Digital Future Society 2020; GeSI/Accenture 2018; Hilty/Bieser 2017).
On the negative side, digitalisation can aggravate ongoing trends that are polarizing income or education level, and encouraging further economic growth that demands additional energy and resource consumption. This, in turn, could affect certain consumption patterns to become more instead of less energy or resource intensive (e.g. WBGU 2019; Lange/Santarius 2020). And with filter bubbles and echo chambers in digital space buttressing polarized discourses on climate change (Williams et al. 2015), successfully arguing sustainability cases is likely to become increasingly difficult. These examples suggest what has been found by more solid studies (e.g. Hilty/Aebischer 2015; Santarius et al. 2020): It is hard to draw an overall conclusion on how digitalisation impacts sustainability. Instead, politics, companies, and individuals must actively shape societal digitalisation processes to maximize their potentials for sustainability. Opportunities, risks and options for policies and actions need to be analysed in more detail.
This journal volume contributes to the endeavour to dive deeper into certain topics and to explore further the nexus of digitalisation and sustainability. In the following, we present the problems and challenges associated with digitalisation for sustainable development in infrastructure and services, hardware and software, energy systems and Artificial Intelligence (AI). Noticeably, the high expectations of digitalisation as a panacea have not yet been fulfilled and they depend heavily on the social, economic and political framework conditions. In particular, the question of what policies for a sustainable digitalisation can look like in distinctive fields of action is examined in the articles of this journal volume.
The European Green Deal sets out a range of critical actions to address the climate crisis: A climate neutral continent by 2050, clean circular economy, transformations and innovation for public infrastructure, the energy sector, building efficiency and more. It also stipulates investments in “environmentally-friendly technologies” and economic growth that is decoupled from resource use.
Diagrams from Dr Alesha Sivartha's Book of Life (1898), Public Domain Review
Artificial Intelligence (AI) is often presented as a powerful solution to fuel this green transition. But is that true?
Aware of the risk that government incentives to boost growth post-pandemic may well undermine many of the necessary investments and reductions to mitigate the climate crisis, we increasingly hear calls for a “green recovery”.
Different implementations of human-centric AI may certainly provide opportunities for change, including when it comes to advancements in medicine, food production, traffic management and more — all of which are highly relevant to managing the climate crisis. At the same time, any implementation of AI builds on massive and still growing volumes of data that need to be stored and processed, which has a significant environmental impact. In addition to mitigating harmful uses of AI that amplify discrimination and bias, undermine privacy, and violate trust online, we need a lot more transparency around its environmental impact, too.
What do we know about AI’s environmental impact?
Illustrative research from Massachusetts Institute of Technology (MIT) showed that training popular natural language processing AI models produced the same CO2 as flying roughly 300 times between Munich, Germany and Accra, Ghana. One of these models is called GPT-2, which was estimated to require 284 metric tons of carbon dioxide (mtCO2e).
In June 2020, GPT-3 was released – a model that is exponentially bigger than its predecessor. GPT-3 builds on 175 billion parameters, whereas the 2019 GPT-2 model builds on “only” 1.5 billion parameters.
In any case, there are countless models and implementations with similar or even bigger scope and larger data sets that all add to the overall environmental impact of AI.
And even just this one model consuming 284 mtCO2e could instead power 33 U.S. homes for an entire year.
In addition, we have to account for the physical presence of data centres which occupy extensive surfaces of land and put significant strain on global water resources, factors that are not consistently reflected in corporate sustainability reports.
Greenhouse Gas emissions (GHG) assessments
Greenhouse gas emissions (GHG) accounting is incredibly complex. And it is currently entirely voluntary for tech companies.
So it is of little surprise that tech companies only rarely publish the information necessary to make such calculations, and if they share findings or results of their impact assessments, methodologies remain vague. In part, this is aggravated by the fact that there is little detail or meaningful guidance about how to measure the environmental impact of digital products like AI — which is stunning, given that it forms the basis upon which we can identify where and how to improve.
Most companies report on the basis of the GHG Protocol, yet there are considerable differences in the (public) accounting of emissions. The GHG Protocol is the most commonly used standard for environmental impact assessments that also provides guidance for how to account for different greenhouse gases, not just carbon dioxide (CO2). It encompasses three scopes: scope 1 assesses direct emissions, scope 2 includes emissions from purchased electricity, heating or cooling, and scope 3 is supposed to span a company’s value chain, including business travel, events, or purchased goods and services, as well as the use of (sold) products.
Some companies only report against scope 1 and 2 (often described as “operational emissions”), while others include scope 3 value chain emissions yet share little about materiality or methodologies.
To give just one concrete example from my personal experience: The difficulty of clear boundaries was also visible in Mozilla’s 2019 Greenhouse Gas emissions report, in which the use of its products, like Firefox, contributed roughly 98% of the organisation’s overall emissions. However, whether people read the news, use their email client, watch cat videos, or shop was not distinguished, instead the assessment accounts for overall time spent online. While insightful for the internet’s impact at large, it will be challenging to mitigate the impact of the organisation’s own digital products with such rough estimates.
What do we need going forward?
Without obligatory reporting and a clear understanding that responsibilities can’t be delegated to consumers, there is little incentive to really meet the sort of ambitious climate targets we need in order to tackle this crisis.
The question is not whether technology has a role to play to fuel both a green recovery and long term societal transformation, but which technologies will make a net-positive difference. To answer that, we need to be in a better position to do our homework and genuinely assess the environmental impact of digital technologies, including AI. Otherwise, any claim that AI supports a green transition will remain unsubstantiated as environmental costs are not properly accounted for.
To put it more bluntly: Positive uses of AI for mitigating the climate crisis can only be net-positive if we know what their own environmental impact is, including training, storing, processing of data and the physical presence of data centres.
Standards and mandatory, transparent reporting
Systemic challenges require nuanced solutions. To innovate sustainably, we must ensure that we have the details we need to make informed decisions, so that we can remain alert and protect against potential risks, including for the environment.
To start, we need better standards for GHG accounting and mandatory, transparent reporting against all scopes and categories of the GHG protocol.
We need regulation for environmental impact assessments in the tech sector, including for digital products like AI. This also means investing in open sourcing emission factors, calculation formulas and tools that do not just approximate but help calculate and determine the impact of digital products, too.
Ultimately, I like to think that in the discussions around privacy and data protection, certainly under GDPR, we have grown more willing to stop and ask: Is everything we can do, really what we should do? This is exactly the mindset we now need with a view to the environmental impact of AI as well: Does the benefit of the suggested solution really outweigh its negative environmental impact? Is it not just possible, but responsible?
Only then will we be able to really live up to the requirements of the EU Green deal, fuel a sustainable recovery, and promote healthy societal transformation to mitigate both the effects of the pandemic and the climate crisis.
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
The effects of climate change are increasingly visible. Storms, droughts, fires, and flooding have become stronger and more frequent. Global ecosystems are changing, including the natural resources and agriculture on which humanity depends. The 2018 intergovernmental report on climate change estimated that the world will face catastrophic consequences unless global greenhouse gas emissions are eliminated within thirty years. Yet year after year, these emissions rise. Addressing climate change involves mitigation (reducing emissions) and adaptation (preparing for unavoidable consequences). Both are multifaceted issues. Mitigation of greenhouse gas (GHG) emissions requires changes to electricity systems, transportation, buildings, industry, and land use. Adaptation requires planning for resilience and disaster management, given an understanding of climate and extreme events. Such a diversity of problems can be seen as an opportunity: there are many ways to have an impact.
In recent years, machine learning (ML) has been recognized as a broadly powerful tool for technological progress. Despite the growth of movements applying ML and AI to problems of societal and global good, there remains the need for a concerted effort to identify how these tools may best be applied to tackle climate change. Many ML practitioners wish to act, but are uncertain how. On the other side, many fields have begun actively seeking input from the ML community. This paper aims to provide an overview of where machine learning can be applied with high impact in the fight against climate change, through either effective engineering or innovative research. The strategies we highlight include climate mitigation and adaptation, as well as meta-level tools that enable other strategies. In order to maximize the relevance of our recommendations, we have consulted experts across many fields (see Acknowledgments) in the preparation of this paper.
Stel je voor: de natuur op aarde wordt behouden, en zelfs verbeterd. Uitgestorven diersoorten komen weer tot leven. En daar hoeft de mens eigenlijk niets voor te doen. Hij kan het beschermen en creëren van natuurgebieden overlaten aan de technologie.
Stel je voor datje delen van de aarde ‘beter’ zou kunnen maken. Dat je vervuilde gebieden zou kunnen schoonmaken, afgebrande bossen zou kunnen laten teruggroeien en uitgestorven dieren opnieuw te voorschijn zou kunnen toveren. En voor dit alles zou je als mens nog maar nauwelijks iets hoeven doen. De gebieden beschermen zichzelf met behulp van geavanceerde, zelf lerende technologie. Het lijkt te mooi om waar te zijn. Of misschien toch…?
In een artikel in het wetenschappelijke tijdschriftCellvragen B. Cantrell, L. Martin en E. Ellis het zich hardop af. De wetenschappers verkennen in dit artikel,Designing Autonomy, de implicaties van een imaginaire ‘wildness creator’, een vorm van artificiële intelligentie (AI) die zelfstandig autonome natuurgebieden creëert en beschermt. Zou dit werkelijk de toekomst kunnen zijn van de conservatiebiologie en van het natuurbehoud op aarde?
Om deze vraag te beantwoorden moeten we eerst kijken hoe zo’n ‘wildness creator’ er in de ogen van deze auteurs uit zou gaan zien. Ze schrijven dat het gaat om een ‘niet-menselijke intelligentie, die oorspronkelijk wel is ontworpen door mensen, maar die in staat is om zelf nieuw strategisch gedrag te ontwikkelen en relaties aan te gaan met verschillende organismen in het ecosysteem’. Het uiteindelijke doel is om een ‘wild ecosysteem te behouden waar mensen geen invloed op hebben. Algoritmen die het systeem controleren leren uit de context en uit het gedrag van organismen en worden niet geprogrammeerd door mensen.’
Mensen zouden er in zo’n gebied niets van merken dat de gehele infrastructuur wordt gemonitord en gereguleerd door de ‘wildness creator’. Ze zouden in hun beleving ‘pure’, ‘ongerepte’ natuur ervaren. Omgekeerd zou ook ‘de natuur’ geen last hebben van mensen, omdat de ‘wildness creator’ alle sporen van mensen direct zou uitwissen volgens zich steeds verder ontwikkelende, steeds slimmer wordende algoritmen.
Voor alle duidelijkheid: de technologie die de wetenschappers voor zich zien, is er op dit moment (nog) niet. Toch vinden ze het tijd dat we ons afvragen wat de gevolgen van dergelijke verregaandedeep learning-methoden zouden zijn wanneer die zouden worden toegepast in de ecologie. Er is namelijk nu al een duidelijke trend zichtbaar in de richting van steeds verder gaande automatisering.
‘Een van onze doelen was om het idee dat technologie “onnatuurlijk” en “tam” is in plaats van natuurlijk en wild ter discussie te stellen’, licht auteur Laura Martin in een e-mail toe. Tegelijk wijst ze erop dat ze ook ethische vragen wilden uitlokken. Zo worden mensen uitgesloten van het beslissingsproces. Bovendien zou ‘macht worden geconcentreerd bij de makers van de wilderness creators’. Als die zelf gaan leren en beslissingen nemen, wie is er dan voor verantwoordelijk en aansprakelijk? ‘In die zin zijn de ethische kwesties vergelijkbaar met die van zelfrijdende auto’s.’
Het management vannatuurgebieden vergt steeds meer menselijk ingrijpen. Denk aan alle drones en andere vormen van beveiliging die mensen inzetten om de overgebleven neushoorns en andere megafauna in Afrika te beschermen. Paradoxaal genoeg kost het reduceren van de invloed van de mens op ecosystemen dus steeds meer en steeds intensiever menselijk management. Waarbij uiteraard de mens telkens bepaalt hoe de natuur ‘hoort te zijn’. Wat is natuur? Wat is wildernis? Allemaal vragen die wij mensen beantwoorden. Met als gevolg dat we ‘wilde natuur’ zoals die is ontstaan door het ‘rewilding project’ in de Oostvaardersplassen prachtig vinden, maar dan liever wel zonder zielige, stervende paarden in de winter. We willen dolgraag de laatste neushoorns in Afrika beschermen terwijl de ecologische functie van deze iconische dieren in het ecosysteem in feite al lang is uitgespeeld.
We kunnen onze menselijke neiging om in te kleuren wat wij onder ‘goede, pure, wilde natuur’ verstaan niet gemakkelijk onderdrukken waardoor we waarschijnlijk niet altijd de beste keuzes maken. In dit licht wordt het idee van een niet-menselijke intelligentie die de rol van beschermer van ecosystemen op zich neemt iets begrijpelijker. Zo’n vorm van intelligentie komt wellicht op ideeën waar mensen simpelweg nooit op zouden komen, gehandicapt als we zijn door onze antropocentrische blik.
Bron: Bloemink S. 2017. Wildernis in algoritmen: Kan artificiële intelligentie de natuur redden? De Groene Amsterdammer.
Digitalisering transformeert wereldwijd economieën en maatschappijen in een razendsnel tempo. Nederland heeft een goede uitgangspositie om de economische en maatschappelijke kansen van digitalisering te verzilveren. De digitale infrastructuur is van wereldklasse, de beroepsbevolking is goed opgeleid en we hebben een traditie van samenwerking, bijvoorbeeld tussen bedrijfsleven, kennisinstellingen en overheid. Tegelijkertijd roept digitalisering ook nieuwe, fundamentele vragen op. Bijvoorbeeld over de bescherming van onze privacy en de toekomst van onze banen.
Om de kansen van digitalisering te benutten en antwoorden te geven op deze vragen moet Nederland voorop lopen met digitalisering. Met onderzoek, met experimenten en met het toepassen van nieuwe technologie. Op die manier versterken we het Nederlands verdienvermogen, kunnen we beter richting geven aan technologische ontwikkelingen en zetten we vol in op de economische en maatschappelijke kansen van digitalisering.
Om voorop te kunnen lopen moeten we ook het vertrouwen van burgers en bedrijven vergroten. Daarom versterken we het fundament – o.a. privacybescherming, cybersecurity, digitale vaardigheden en eerlijke concurrentie - voor digitalisering. De uitdaging bij deze transformatie is om iedereen binnen boord te krijgen én te houden. Op de arbeidsmarkt, maar ook in de samenleving als geheel.