Use of ICTs to confront climate change: analysis and perspectives

The effects of climate change, including temperature and precipitation changes, the retreat of ice sheets, and rising sea levels are more evident today. It emphasizes that greenhouse gases are the primary drivers of these changes. In this context, some international organizations such as the United Nations (UN) and others have been making significant efforts to combat these effects and have considered information and communication technologies (ICTs) as an alternative for monitoring and mitigating climate change. However, the role of ICTs in climate change has not been analyzed in detail. Accordingly, this article presents research progress on the role of ICTs in climate change monitoring and evidence that ICTs are effective tools for reducing greenhouse gas emissions from different sectors. Additionally, this article provides a cost-benefit analysis of ICT applications in various sectors, emphasizing the Sustainable Development Goals (SDGs).

1 Introduction

Climate change is one of contemporary society’s most complex and pressing global environmental challenges (Nordhaus, 2019; Pörtner et al., 2022). It is characterized by changes in climate patterns, such as long-term trends in temperature and precipitation, the shrinking of global ice caps, and rising sea levels, among other phenomena (Abbass et al., 2022; FitzGerald and Hughes, 2019; Kamal, 2022; Okezie, 2021). Greenhouse gases (GHGs) are identified as the main drivers of this climate crisis, effectively trapping heat within the atmosphere and propelling global warming. Since the 1970s, GHG emissions have increased by over 70%, which has led to noticeable changes in global weather patterns, highlighting the severity of the issue (Lipczynska-Kochany, 2018; Murshed and Dao, 2022; Swain et al., 2021; VijayaVenkataRaman et al., 2012). As a result, it is expected that the worldwide impacts of climate change, coupled with contributing factors such as increased deforestation, will enhance the likelihood of floods, droughts, and erosion. This is due to significant disturbances in atmospheric and oceanic conditions, affecting natural ecosystems and human habitats (Islam and Winkel, 2017).

The main sources of GHG emissions are natural systems and human activities. Natural systems contributing to GHG emissions include phenomena like wetlands, forest fires, earthquakes, and volcanoes (Toulkeridis et al., 2017; Toulkeridis et al., 2019; Toulkeridis and Zach, 2017; Yue and Gao, 2018), while human activities predominantly involve land use changes, and deforestation (Barreto-Álvarez et al., 2020; Cayambe et al., 2023; Heredia-R et al., 2021a; Heredia-R et al., 2022). Furthermore, burning fossil fuels is one of the main causes of GHG emissions. This background sets the stage for examining how Information and Communication Technologies (ICTs) can play a crucial role in monitoring and mitigating climate change’s adverse effects, providing a framework for the subsequent discussion.

Several global agreements have supported efforts to monitor GHG emissions over time. The first significant global pact was the Montreal Protocol, signed in 1987, which aimed to protect the ozone layer by curbing substances that lead to its depletion (Chipperfield et al., 2015; Egorova et al., 2013; Goyal et al., 2019; Morgenstern et al., 2008; Velders et al., 2007). The protocol targeted chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), which are major contributors to ozone layer depletion (Ibárcena and Scheelje, 2003). The subsequent Kyoto Protocol, adopted in 1997 and enacted in 2005, focused specifically on reducing emissions of five greenhouse gases, including methane (CH₄) and carbon dioxide (CO₂), nitrous oxide (N₂O), and fluorinated gases such as hydrofluorocarbons (HFCs) and sulfur hexafluoride (SF₆) to mitigate climate change (Gerden, 2018; Hussain et al., 2019; Leggett, 2020; Murshed, 2022; Murshed et al., 2020; Sovacool et al., 2021; United Nations Framework Convention on Climate Change, 2009). Most recently, the Paris Agreement, signed in 2015 and ratified by 185 countries to date, aims to maintain global temperature rise this century well below 2 degrees Celsius (Hoegh-Guldberg et al., 2018).

A report published by the United Nations Environment Programme (UNEP) in 2018 highlighted that total global greenhouse gas emissions had reached 55.3 gigatons of equivalent CO₂ (Maertens, 2018) of which 37.5 gigatons were due to CO₂ emissions from fossil fuel combustion and industrial processes. This marked a 2% rise in emissions in 2018 alone, compared to the annual growth rate of 1.5% observed from 2010 to 2018. The increase in emissions was primarily due to higher energy demands. Emissions from land use changes were reported at 3.5 GtCO₂ for the same year. Combined, emissions from fossil fuels and land use changes constituted about 74% of all global greenhouse gas emissions in 2018 (United Nations Environment Programme, 2020). Methane emissions increased by 1.7% in 2018, up from the decade’s annual growth rate of 1.3%. Nitrous oxide emissions from agriculture and industry grew by 0.8% in 2018, slightly lower than the decade’s annual growth rate of 1%. Furthermore, emissions of fluorinated gases saw a significant spike of 6.1% in 2018, compared to a decade-average increase of 4.6% (United Nations Environment Programme, 2020). Global warming is already impacting, necessitating immediate and significant efforts in adaptation, particularly in poorer countries which feel the brunt of the effects more severely due to their lesser capacity to adapt. The socio-economic and environmental context and availability of information and technology significantly influence these countries’ ability to respond effectively to climate change (Waisman et al., 2019).

Due to industrialization, climatic variations have been evident which has severe implications for biodiversity (Bellard et al., 2012; Mantyka-pringle et al., 2012; Pawson et al., 2013). A current report by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) mentions that the extinction of one million species is threatened in the coming years if important changes are not implemented in land use, environmental protection, and climate change mitigation. This detailed analysis reveals alarming statistics: about 85% of wetlands have already been lost; since the late 19th century, nearly half of all coral reefs have been eradicated; 90% of all livestock breeds have disappeared; between 1980 and 2000, deforestation affected 100 million hectares of tropical forest, with an additional 32 million hectares lost between 2010 and 2015; 23% of the planet’s land is now considered ecologically degraded and unusable; the decline of pollinators puts food production worth between $235 billion and $577 billion at risk annually; and the destruction of coastal environments such as mangrove forests endangers the lives of up to 300 million people (Lehikoinen et al., 2019; Oliver and Morecroft, 2014; Thom et al., 2017; Watson et al., 2019).

Technological advancements have significantly increased the use of information and communication technologies (ICTs), accounting for approximately 2–2.5% of global GHG emissions. The hundreds of millions of computers and over a billion televisions that are never turned off at night in homes and offices contribute 40% of the total GHG produced by ICT. In contrast, servers and refrigeration systems contribute 23%, fixed communication lines generate 15%, mobile communications 9%, local area networks, and telecommunications sites 7%, and, lastly, printers 6% (see Figure 1) (Uddin et al., 2017). Despite this, the contribution of ICTs to the gross domestic product (GDP) is much larger than their environmental impact. For instance, in the United States, the ICT sector represents about 8% of the GDP, highlighting that its primary output is information rather than physical goods (Kelly and Adolph, 2008).

Figure 1

Figure 1. Global CO₂ emissions from ICT (Uddin et al., 2017).

The impact of climate change is evident across different sectors including agriculture, health, and infrastructure, necessitating the development of strategies to mitigate these negative effects (Camilloni, 2018; Canaza-Choque, 2019; Hasegawa et al., 2018; Malhi et al., 2020; van Vuuren et al., 2018). For example, droughts and floods can severely impact food production, human health, and infrastructure, while climate change can spread diseases and damage ecosystems. The potential health impacts are profound, including increased mortality, compromised food security, and reduced worker productivity (Carter et al., 2012).

Furthermore, the devastating effects of climate change are prompting cities to develop strategies to lessen these impacts (Clayton, 2020; Leichenko and Silva, 2014; Maldonado et al., 2013). As urban populations grow, so does energy demand, presenting new challenges that add a layer of complexity to sustainability efforts. This dynamic underlines the importance of managing and digitizing data to control natural resources from the individual level to the entire city (Balogun et al., 2020).

The data generated electronically through devices and the transmission, processing, and interpretation of this information constitute the backbone of ICT. The Internet of Things (IoT) facilitates the interconnection of devices and sensors, enhancing the interoperability of systems like big data, cloud computing, edge computing, the semantic web, and data storage. These technologies enable applications in smart healthcare, smart transportation, smart cities, and smart agriculture, among others utilizing databases for real-time monitoring of natural phenomena, employing clean technologies, and disseminating information are all part of this digital innovation (Munang et al., 2013). In this context, ICTs indirectly contribute to controlling greenhouse gases, monitoring environmental changes, managing food resources, preventing deforestation, enhancing energy efficiency, and improving waste management (Lee and Mwebaza, 2022). Global communication networks facilitate timely decision-making for preventing, correcting, and supporting emergency measures before, during, and after environmental crises (Delina, 2020). Moreover, we are in the era of a digital revolution, where big data and data analytics play a pivotal role. The digitization of environmental sciences via ICT products offers a modern framework to tackle the challenges and opportunities associated with climate change (Ballantyne et al., 2016; Gangopadhyay et al., 2019; Koliouska and Andreopoulou, 2020).

ICTs also play a crucial role in monitoring and predicting climate change, as well as aiding adaptation efforts (Ajwang and Nambiro, 2022). A significant contribution of ICTs in meteorology and the prediction and detection of natural disasters comes through advanced observation systems like those operated by the World Meteorological Organization (WMO), which tracks atmospheric and weather changes (World Meteorological Organization, 2009, 2010, 2019). Over the years, the push for technological tools to mitigate the adverse impacts of climate change has grown, with entities such as the International Telecommunication Union (ITU) utilizing ICTs for monitoring climate change, and in forecasting, detecting, and mitigating the effects of typhoons, earthquakes, tsunamis, and other man-made disasters (Kelly and Adolph, 2008).

The role of ICT in climate and weather monitoring is exemplified by the structure of the World Meteorological Organization’s (WMO) World Weather Watch (WWW) program. This program consists of three integral layers utilizing various ICT components and applications: (Zemp et al., 2021).

• The Global Observing System (GOS) captures observations of the Earth’s atmosphere and surface, including ocean surfaces, from locations worldwide and outer space. The GOS serves primarily as a hub for relaying data from remote sensing devices mounted on satellites, aircraft, radiosondes, and weather radars, both on land and at sea (see Figure 2) (Moltmann et al., 2019; World Meteorological Organization, 2010; Tanhua et al., 2019). In fact, GOS plays a crucial role in supporting informed decision-making across multiple sectors by providing comprehensive, accurate, real-time data on Earth systems. It combines observations of land, ocean, atmosphere and space to provide useful information for environmental monitoring, disaster response and sustainable development (Zemp et al., 2021).

• The Global Telecommunication System (GTS) integrates radio and telecommunications equipment to facilitate real-time data exchanges of vast volumes of meteorological data and related information among national and international meteorological and hydrological centers (World Meteorological Organization, 2009).

• The Global Data Processing System (GDPS) relies on an extensive network of mini, micro, and supercomputers to process large volumes of weather observation data, producing vital outputs such as weather analyses, warnings, and forecasts (World Meteorological Organization, 2019).

Figure 2

Figure 2. Image showing the Global Observing System. Source: Authors.

On the other hand, the role of ICTs extends beyond surveillance; they also contribute significantly to the reduction of carbon dioxide emissions by diminishing or replacing the need for travel. The ICT sector provides a range of tools and services that can substitute for travel, especially in the context of business. These range from basic technologies such as email, phone calls, and text messaging to more advanced solutions like high-speed video conferencing (Obringer et al., 2021). As a result, the ICT industry plays a pivotal role in reducing the carbon footprint, a key metric for quantifying the total greenhouse gases emitted—either directly or indirectly—by an individual, organization, or product over a specific period.

Regarding the urban population, a report published by the United Nations in 2028 indicates that two-thirds of the world’s population will soon reside in urban areas due to the continued growth of the urban population (United Nations, 2018). In such environments, ICT tools are crucial for the intelligent management of essential services, including street and home lighting, waste collection, crime prevention, the maintenance of urban facilities and parks, as well as mobility and transportation of goods, unmanned vehicles, and public parking. The integration of the Internet of Things (IoT) within the urban landscape, through sensors and electronic devices interacting with existing communication infrastructures, facilitates the gathering and analysis of vast amounts of data. This data collection and analysis help in generating projections and identifying patterns that can optimize the use of available resources (Ahmad and Zhang, 2021; Cheng et al., 2022).

In conclusion, ICT tools appear to offer viable alternatives for monitoring and mitigating climate change. Nevertheless, it is crucial to conduct further reviews of existing literature on ICT applications in climate change monitoring and their role in reducing GHG emissions in future scenarios. This research aims to provide a comprehensive overview of both current and potential future applications of ICTs in adapting to and mitigating climate change.

2 Materials and methods

A literature review was conducted using the analytical-synthetic method as a general approach. The data collection relied on compiling a range of bibliographic documents, including major and minor works, references, and study materials. Through guides or repertoires of information sources, it was possible to systematize the conceptual and normative bases giving a better analysis of the information consulted (Valencia et al., 2017). Furthermore, the study examined theoretical-scientific data through units of analysis such as documents, scientific journals, books, texts, and presentations at congresses to conceptualize some terms such as climate change, GHG, information and communication technologies (ICTs), flooding, droughts, glacier melt, ocean acidification, deforestation, and smart systems such as smart city, smart building, smart energy, smart agriculture, smart services, and smart work (teleworking). The methodological process was systematically approached in two stages:

Phase 1. Collection of information: it consisted of searching the database of two international indexers, Scopus and Web of Science, as well as Google Scholar. Likewise, study of the reports of organizations of regional and international significance was unavoidable, such as the Intergovernmental Conference on Climate Change (IPCC), the United Nations Environment Programme (UNEP), the United Nations Framework Convention on Climate Change (UNFCCC), the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the World Glacier Monitoring Service (WCMS) and the Global e-Sustainability Initiative (GeSI).

Phase 2. Analysis and interpretation: the study required an investigation, systematization, review, and bibliographic analysis of at least 300 texts of scientific and academic literature, to draw a logical inference about the study. The compilation of theoretical foundations, conclusions, and results found by the various authors allowed us to identify relevant approaches for our research topic.

The search of articles was carried out as follows: a repertory of articles from 2008 to 2023 was collected categorizing the use of ICTs in climate change monitoring and as well as the use of ICTs in CO₂ emissions reduction. For instance, the role of ICTs in monitoring floods, droughts, glacier melt, deforestation, etc., and the role of ICTs (smart city, smart building, smart transport, smart agriculture, etc.,) in CO₂ emissions reduction. The articles were selected by type of article (research article) and subject area (environmental science). A sample of 100 and 520 corresponding to two categories was obtained. Then, in the second category, it was necessary to cluster smart cities, smart buildings, smart energy, and smart transportation into a single sector because these sectors are closely related. Moreover, this allowed us to remove duplicated information from the database and obtain a more reliable sample. After removing duplicated information, a sample of 300 was obtained. Finally, the selection criteria were refined by selecting the most recent articles and the number of citations achieving a sample of 200. The total sample obtained was 300 including the two categories.

3 Results and discussions

3.1 Use of ICTs to monitoring climate change

This part presents results and analyses of scientific reports on ICT applications in climate change monitoring/adaptation. The analyses are restricted to the role of ICTs in flooding, droughts, glacier melting, ocean acidification, and deforestation.

3.1.1 Flooding

Torrential rainfall has become heavier and more frequent in the last few years. This is because climate change leads to warmer natural conditions resulting in increased evaporation and transpiration by vegetation. Thus the concentration of water vapor in the atmosphere tends to be higher (Gärdborn and Xia, 2018). A consequence of the rainstorms is flooding, a high flow or inundation of water that causes damage (Adams, 2016). The environment and human society are both affected by flooding. In this context, ICT tools have been proposed as an alternative for predicting and warning of disaster situations, together with emergency decision-making.

For example, the National Aeronautics and Space Administration (NASA) has developed an app simulator demonstrating what could happen to 293 coastal cities worldwide (Larour et al., 2017). This app shows climatic aspects and possible floods in the next 100 years. NASA researchers have warned for years that rising ocean levels will cause increasingly catastrophic flooding in many parts of the world and that within a few decades, these regions could be entirely underwater. The data for this simulation was derived from an advanced mathematical property of adjunct systems that determines the exact gradient of sea-level fingerprints concerning local variations in ice thickness from all the world’s ice drainage systems. This conventional method is based on the following Equation (1) (Larour et al., 2017):

$\begin{array}{ll}\Delta S_{\mathrm{local}}\left(t\right)=\underset{\mathrm{ice}}{{\displaystyle \int }}\frac{\mathrm{d}S}{\mathrm{d}H}\left|\left(\theta ,\lambda \right)\mathrm{local}·\Delta H\left(\theta ,\lambda ,t\right)\mathrm{d}A+\delta S_{\mathrm{local}}\left(t\right)\right.& (1)\end{array}$

where “local” refers to the location of the port city of interest, “ice” refers to glacial changes, (θ, λ) represents the geographic coordinates on the surface of Earth, t is the time, dA is an elementary integration area on the globe, and ∆S_local(t) is the quantity coastal planners wish to assess, given a set of observations or projections about variations in ice thickness, ∆H, around the world. In this equation, dS/dH|_local corresponds to the value of the Jacobian at a specific location, also referred to as the gradient of the sea-level fingerprint.

By comprehensively mapping these fingerprint gradients, a diagnostic tool, called gradient fingerprint mapping (GFM), can be created to easily assess future coastal flooding or emergencies (Larour et al., 2017; Stammer et al., 2013). Figure 3 shows some approaches for New York. The gradient [dS_NY/dH (in 10⁻³ μm per km²) of sea level in New York (S_NY) with respect to (H) changes in glaciated areas] has been processed for all glaciated regions in the world. According to researchers, the cause of concern in New York is the ice sheets that extend across its northern and eastern parts.

Figure 3

Figure 3. Sensitivity of sea-level rise (SLR) to worldwide variations in ice thickness (Larour et al., 2017).

Another example of using ICTs to predict and warn of flood disasters is the European Flood Awareness System (EFAS). It is designed to provide early warnings of potential floods across Europe using advanced ICT systems and predictive modeling tools (McCormick and Salamon, 2023).

In July 2021, heavy rainfall caused severe floods in Germany and Belgium and the EFAS system played an important role in warning and predicting the disaster. The system predicted the potential flooding days before the event, and authorities were alerted. Although the flooding was devastating, the early warnings helped reduce the potential loss of life by enabling timely evacuations and response efforts (Thieken et al., 2023).

3.1.2 Droughts

According to the United Nations, droughts are among the most feared natural phenomena and often impact people, the economy, and ecosystems. Droughts reduce food production and water availability due to a lack of rainfall/surface or groundwater. Droughts also destroy livelihoods and lead to untold human suffering and loss of life (Crossman, 2018).

Several ICT technologies may be used to detect upcoming droughts, as well as to mitigate the consequences of drought. The concept of mitigation in this case does not have to do so much with stopping the drought from happening but rather with preparing society in such a way that it can deal with droughts (Hussain et al., 2005). Predicting and monitoring potential droughts can draw upon Remote Sensing (RS), which is usually satellite or aircraft-based, and detects changes from a distance through “optical, acoustical or microwave” signals (Schowengerdt, 2006). The Global Positioning System (GPS) and Wireless Sensor-Based Networks (WSN) are also used. RS, GPS, and WSN could benefit from further improvements in network technologies such as 5G due to potentially shorter latency and higher bandwidth, making it easier to supply larger amounts of data for analysis (Gärdborn and Xia, 2018; Hussain et al., 2005; Schowengerdt, 2006).

Remote sensing technologies, particularly satellite-based systems, are extensively used to assess the health of vegetation and crops in drought-prone areas. Sensors on satellites like Sentinel-2 (by the European Space Agency) capture multi-spectral images of the Earth’s surface, which are analyzed to determine vegetation health through indices such as the Normalized Difference Vegetation Index (NDVI).

ICTs, especially through Geographic Information Systems (GIS) and satellite data, provide geospatial analysis of drought-affected areas. By overlaying drought data (soil moisture, precipitation anomalies) with population maps, ICT tools can help authorities assess which regions and populations are most vulnerable to drought conditions.

• This analysis enables better resource allocation (e.g., water distribution or food aid), prioritizing areas most in need of support. For instance, drought conditions can be mapped against human population density, agricultural activity, or infrastructure, providing a clear picture of where interventions are most urgent.

• Remote sensing data combined with climate models can also predict future drought patterns, helping long-term planning for water management and agricultural policy adjustments.

Drought forecasting is a potential adaptation role that ICTs can play in droughts. Likewise, the role of ICTs is to analyze what happens to society when a drought occurs and to take measures to prepare from the beginning with adequate adaptation strategies. However, people at risk of drought and other natural disasters should be informed in advance to be prepared for possible consequences.

3.1.3 Glacier melting

The enhanced greenhouse effect is the main cause of glacier melt. It is particularly due to changes in heat or high temperatures which affect the melting of the glacier (Glick, 2004; Zillman, 2009). In this context, ICT can provide a powerful tool for transmitting hydro-meteorological information to predict, prepare, and adapt to such events. However, in remote regions, such as mountains, the poles, and islands, preventive and adaptive measures are often limited by data availability and lack of data networks (de Jong, 2013).

ICT tools have been used for glacier monitoring. For example, tracking of the movement of the ice sheets of the Trift Glacier, Switzerland between 2004 and 2005 was carried out using sensors based on ICT equipment (telemetry). In addition, the World Glacier Monitoring Service (WGMS) (WGMS, 2021) uses a multi-tiered, integrated approach to documenting glacier variation, which involves bringing together satellite and GPS remote sensing data with aerial photography, in situ measurements, and ice mass balance computer models (Garza and Hidalgo, 2017).

According to the IPCC (Hock et al., 2019), the duration of snow cover has decreased in almost all areas, particularly at lower elevations, by approximately 5 days per decade on average, within a probable range of 0 to 10 days per decade. Additionally, the depth and extent of snow at lower elevations have decreased, though there is significant annual variability. Mass change of glaciers in all mountain regions (excluding the Canadian and Russian Arctic, Svalbard, Greenland, and Antarctica) was very likely −490 ± 100 kg m⁻² year⁻¹ (−123 ± 24 Gt year⁻¹) in 2006–2015.

Alterations in snow and glaciers are modifying the volume and timing of runoff in regions dependent on snow and glacier-fed river basins, which is affecting local water resources and agriculture. In the polar areas, there is a significant loss of ice and rapid changes in the oceans. These shifts in the polar regions have global implications, influencing diverse aspects such as climate-related changes in Arctic hydrology, wildfires, and sudden thaws, which affect vegetation, water availability, and food security. Additionally, there has been a reduction in snow and lake ice cover, with June snow cover decreasing by 13.4 ± 5.4% per decade from 1967 to 2018. Runoff into the Arctic Ocean from Eurasian and North American rivers has increased by 3.3 ± 1.6% and 2.0 ± 1.8%, respectively, from 1976 to 2017. The future of the polar regions will be markedly different from today, with the extent and nature of these changes heavily dependent on the pace and scale of global climate change (Meredith et al., 2019). Scientists using increasingly detailed field surveys and remotely sensed data are rapidly improving the understanding of glacier melt. However, assessing the effect this has on water resources and poverty is still fraught with difficulties.

3.1.4 Ocean acidification

Ocean acidification, driven by carbon dioxide (CO₂) absorption from the atmosphere, represents a significant risk to marine ecosystems and biodiversity. When the ocean absorbs CO₂, it interacts with seawater and forms carbonic acid, causing a reduction in pH, increased acidification, and altering carbonate chemistry (Falkenberg et al., 2020). Oceans comprise about 70% of the earth’s surface and are getting acidified because humans emit CO₂ and other gases in different processes, e.g., driving a car (Gattuso and Hansson, 2011). A quarter of the emitted CO₂ ends up in the oceans and the consequences of acidified oceans are reported as follows (Rockstrom, 2010):

• Reduction of important matter for marine species such as the formation of shells and skeletons.

• Some species, e.g., shellfish, corals, and plankton will have difficulties growing and surviving.

• The fish stock could decrease since they have difficulties surviving, therefore the food source may decrease.

Ocean acidification can change the abundance and chemical composition of harmful algal blooms so that the toxicity of shellfish increases, thereby negatively affecting human health (Falkenberg et al., 2020). Furthermore, acidification could cause uncoupling of biological and environmental signals, leading to reproductive failure with significant consequences for population dynamics in marine ecosystems (Padilla-Gamiño et al., 2022).

Regarding the fish stock, ICT applications on smartphones could be useful because they can provide computational knowledge so fishermen can estimate the number of fish they can catch without putting the fish stock at risk (Oviedo and Bursztyn, 2017). In other words, applications can be integrated with databases and monitoring systems that provide real-time data on fish populations, seasonal movements, and reproduction cycles, helping fishermen avoid overfishing certain species or areas.

ICT applications are already used on a large scale for monitoring systems. For example, the European Union (EU) uses the Vessel Monitoring System to track vessels through satellites and communication systems (Lee et al., 2010). Vessel Monitoring Systems (VMS) have been largely used to map the distribution of fishing activities. According to Gerritsen (2023), mapping areas with low levels of fishing activity can be interesting to avoid conflicts between fishing and other uses like offshore renewable energy or to protect relatively pristine ecosystems from increasing fishing pressure. Wada et al. (2013) mention that in Indonesia, the monitoring of the production and distribution of fish can be improved through ICT applications. That is throughout the chain from hatching to delivering the product. In the same way, Kimbahune et al. (2013) mention that through smartphone applications, optimal fishing spots can be monitored, and thus the use of diesel fuel can be reduced. Also, Vessel Monitoring Systems (VMS) have revealed increased fishing efficiency following regulatory changes in demersal longline fishery (Watson et al., 2018).

Some case studies are presented in the following: Ji and Li (2021) analyzed lighting fisheries in China based on VMS data transmitted through the BeiDou navigation system, and the research results showed a very significant positive between the Lighting of fishing activities and catch rate. Also in Indonesia, a methodology to compare Vessel Detections (VBD) from the Visible Infrared Imaging Radiometer Array (VIIRS) with Vessel Tracking System (VMS) footprints has been proposed. The process involves predicting the likely location of VMS vessels at the time of each VIIRS data collection with an orbital model. If a VBD record is found within 700 m and 5 s of the predicted location, it is marked as a match. The cross indicates that 96% of matches occur while the boat is fishing (Hsu et al., 2019). To better understand the distribution of fishing efforts across artificial and natural reef types in the Gulf of Mexico, Gardner et al. (2022) linked VMS data from commercial reef fish vessels with high-resolution habitat maps for an iconic species, red snapper (Lutjanus campechanus). The findings revealed that approximately 46% of commercial red snapper landings originated from artificial structures. However, exploitation was uneven, with several concentrated hotspots on natural reefs located along the continental shelf break and offshore areas in the Northeast Gulf of Mexico. Regional fishing patterns also varied significantly: in Florida, nearly 91% of landings came from natural reefs, whereas around 75% of landings in the other Gulf of Mexico states were associated with artificial structures. Researchers highlighted that these patterns suggest a potential risk of localized depletion for red snapper populations.

Additionally, Autonomous Underwater Vehicles (AUVs) equipped with sensors and automation technology are used for odometry over coral reefs in Australia. Odometry refers to the process of estimating the position and orientation of the AUVs over time while they navigate the underwater environment, such as coral reefs (Bellavia et al., 2017; Qin et al., 2022). It is a central component of accurate and repeatable monitoring operations. These AUVs play a crucial role in collecting video data and communication, facilitated by ICTs and IT (Dunbabin et al., 2005; Dunbabin and Allen, 2007).

In essence, ICT applications enable monitoring and collection of ocean data on both local and global scales. They also offer a valuable tool for mitigating the impact of climate change on ocean acidification by providing essential feedback on fish stocks.

3.1.5 Deforestation

Deforestation is a significant contemporary environmental issue, spurred by human activities such as land development for construction, agriculture, livestock rearing, and resource extraction like timber and palm oil. While these activities benefit food production and industry, deforestation has detrimental effects on the environment. Forests, especially rainforests, capture greenhouse gases, generate water vapor, and mitigate water pollution. They also harbor diverse ecosystems. The removal of forests can result in climate change, desertification, soil erosion, reduced crop yields, flooding, and snow avalanches, and indirectly contribute to increased greenhouse gas levels in the atmosphere (Farinotti et al., 2020; Gärdborn and Xia, 2018). Furthermore, deforestation affects the ecosystem/environment due to the loss of carbon uptake and storage (Li et al., 2022).

Studies such as Water Resources Research highlight the impact of deforestation, showing that extensive forest removal in snowy regions can double the occurrence of large floods in nearby streams and rivers by accelerating snowmelt through sunlight exposure (Green and Alila, 2012). Another study by Borrelli et al. (2017) underscores the severe consequences of soil erosion from deforestation, leading to land degradation, loss of fertility, and various off-site effects like sedimentation and waterway pollution.

The FAO-led Global Soil Partnership reported a staggering annual soil erosion of 75 billion tonnes from global arable lands, causing an estimated financial loss of US $400 billion (Caon and Vargas, 2017).

Regarding forest fires, they release atmospheric carbon dioxide (CO₂) and are therefore responsible for greatly increasing the pace of climate change (Singh, 2022). Also, larger fires have been associated with greater burn severity, as measured by greater combustion of organic carbon surface, suggesting that they result in greater carbon dioxide (CO₂) emissions per unit area burned and, therefore, have a greater biogeochemical climate warming impact (Zhao et al., 2024).

Understanding land use and land cover (LULC) changes is vital for comprehending the effects of natural and human-induced processes like climate change, deforestation, and urbanization on the Earth’s surface (García-Álvarez et al., 2022). LULC change is also important for understanding environmental issues related to surrounding landscapes. Remote sensing data are the primary sources used extensively for LULC analysis. Remote sensing combined with Geographic Information System (GIS) has been used extensively in mapping LULC dynamics (Pandey et al., 2021). For instance, a better understanding of land dynamics requires LULC information to determine changes in natural resources (Heredia-R et al., 2021b), in support of Target 3 of Sustainable Development Goal (SDG) 15. “By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world” (Sims et al., 2017). Regarding forest issues and threats, ICT applications can be useful. ICT applications can be used to map forest resources, monitor forest risks, prevent illegal logging and fires, raise awareness of the need for sustainable forestry practices, improve forest governance as well as linking forest communities to achieve sustainable forest management, increase transparency, public participation, and strengthen land rights (Castrén and Pillai, 2017; Reynolds et al., 2005). In this context, many studies on forest monitoring have been conducted. For instance, a study on mapping deforestation using a mobile phone application was developed by Tuukuo (2018). It combines ICT and handheld devices which enable local communities to monitor their forest efficiently and cost-effectively. The results of this study enabled the determination of different issues related to deforestation like logging, infrastructural development, forest to agriculture land conversion, and unplanned settlement.

Forest monitoring has been carried out thanks to the development of ICTs with emerging technologies such as laser scanners (LIDAR) or unmanned aerial vehicles commonly called drones. LIDAR is generally used to make estimates of the available wood inventory such as the number of trees per hectare, tree height, and trunk diameter (Seleznovs et al., 2019). A more precise knowledge of the terrain, the water flows, and the forest inventory contributes to better planning a harvest (Choudhry and O’Kelly, 2018). Drones are increasingly used in the forestry industry to perform observation and terrain mapping tasks when equipped with the mentioned LIDAR. Furthermore, they can also be equipped with thermal cameras or other devices to detect outbreaks of pests and diseases or give early warnings in the event of fire (Baena et al., 2018).

Based on case studies, it is clear that forest monitoring through ICT applications can contribute to decreasing deforestation in the world. However, the costs of using these techniques can be a limitation.

3.2 Summary of ICT solutions for each indicator of climate change

Table 1 outlines specific climate change indicators to assess the possible contribution of ICTs in climate change monitoring.

Table 1

Table 1. Contribution of ICTs to monitor climate change.

3.3 Use of ICTs to decrease greenhouse gas emissions

Multiple studies have pointed out the potential of the ICT sector to lower worldwide greenhouse gas emissions (Ajwang and Nambiro, 2022; Bastida et al., 2019; El-Bawab, 2021; Majeed, 2018; Mickoleit, 2010). For instance, research from the Global e-Sustainability Initiative (GeSI) suggests that ICT applications could help avoid around 20% of annual GHG emissions by 2030 through more intelligent energy usage by businesses and consumers (GeSI, 2015).

Another study highlights that ICTs can cut global greenhouse gas emissions by up to 15% by 2030. This estimate stems from a scenario with high reduction potential, incorporating various ICT solutions across sectors like energy, buildings, transport, and agriculture (see Figure 4). The findings indicate a significant reduction of 10 GtCO_2, or approximately 15% of global GHG emissions by 2030 (Malmodin and Bergmark, 2015).

Figure 4

Figure 4. A high reduction potential scenario for 2030 per the ICT solution category (Malmodin and Bergmark, 2015).

To substantiate these claims, a literature review was conducted to explore the role of ICTs in diverse sectors.

3.3.1 Smart city

A smart city employs an infrastructure primarily reliant on ICTs to enhance efficiencies and enhance the sustainable quality of life for urban residents (Lai et al., 2020). This ICT framework heavily involves intelligent networks comprising interconnected machines and objects that transmit data wirelessly via the cloud. Cloud-based IoT applications play a substantial role in receiving, analyzing, and managing real-time data, aiding municipalities, businesses, and citizens in making informed decisions to enhance overall quality of life (Alam, 2021).

Residents engage with the smart city through various means such as smartphones, mobile devices, connected vehicles, and homes (see Figure 5). Integrating devices and data with the city’s physical infrastructure and services can lead to cost savings and sustainability improvements. Through IoT integration, communities can enhance energy distribution, optimize waste management, alleviate traffic congestion, and even enhance air quality (Kramers et al., 2014).

Figure 5

Figure 5. Image showing some benefits of a smart city. Source: Authors.

Regarding air quality, smart cities play a pivotal role. A recent study conducted in China affirms that in pilot cities, per capita CO₂ emissions have significantly decreased due to smart city development. In addition, smart cities have considerably enhanced the energy efficiency of cities and accomplished CO₂ emission reductions, principally through energy-saving effects (Guo et al., 2022).

In contrast, building and maintaining smart cities can be costly in terms of materials and cybersecurity. However, these costs underline the importance of planning, with a careful balance between initial investments and long-term maintenance to create a sustainable and efficient smart city.

In short, a smart city is an innovative city that uses ICTs and other means to improve life quality, the efficiency of the operation of urban services, and competitiveness, while ensuring that the needs of present and future generations are met regarding different aspects such as the economy, social and environmental conditions.

3.3.2 Smart transport

Studies on smart transport for reducing CO₂ emissions have been reported in the literature. For instance, Nijkamp and Kourtit (2013) argued that smart transport not only contributes to reducing the ecological footprint, congestion, and accidents but also to sustainability. Furthermore, Contreras and Platania (2019) mentioned that in a holistic smart city initiative aimed at mitigating climate change, the transport sector would be the best beneficiary in terms of CO₂ emission reduction.

Beyond physical infrastructure, smart transportation technologies aim to reduce emissions by minimizing driving times. Numerous global initiatives for smart parking have been deployed to track parking space availability in real-time and guide drivers accordingly. These measures reduce the time drivers spend searching for parking, which in turn decreases traffic congestion and emissions. For instance, a case study in San Francisco documented a notable decrease in the time drivers spent searching for parking, leading to lower emissions and less congestion (Alemi et al., 2018).

Long journeys in electric cars are often stressful due to the difficulties in charging a vehicle, particularly when driving from one country to another (Liu et al., 2015). In this way, the European Union has been developing an ICT network mobility project called NEMO which enables those vehicles to be plugged into charging points in any EU country. The network will make it easy for charge point and grid operators, drivers, and providers of payment, navigation, and other related services (Fanti et al., 2017; Morgan, 2012). Furthermore, it could reduce air pollution from transport since this ICT application will encourage motorists to use electric vehicles.

Additionally, there is research related to the smart use of roads, focusing on the use of ICTs for identification of traffic bottlenecks and the optimization of the use of road capacity, in both cases, helping to the reduction of travel times and for instance the decarbonization of road transport (Ahjum, 2020; Džupka and Horvath, 2021). For instance, a study performed in an experimental area in China shows that after using a big data intelligent traffic signal dynamic timing optimization control platform, the travel time was reduced by 15%. Furthermore, the travel time was reduced by 10% during the off-peak period (Wang et al., 2019).

Another study, performed in the UK by innovITS (Pearson, 2013), comes to a similar conclusion. ITS (Intelligent Transport Systems) measures related to fleet operations and management were found to improve travel time by 2–15%. Interestingly, this study showed a reduction in vehicle emissions of 5–20%.

3.3.3 Smart energy systems

A fundamental component of smart city infrastructure is the implementation of smart energy systems (Hayat, 2016). These systems not only offer real-time monitoring but also incorporate a smart grid that supports both centralized and decentralized power systems. Moreover, smart energy systems play a role in combating climate change. For example, According to Hunter et al. (2018) the increased efficiency of ICT-enabled smart energy systems, along with their capability to deliver detailed consumption data, is expected to decrease energy usage and subsequently reduce carbon emissions. Parks (2019) noticed that smart grids could help increase the integration of renewable energy sources, as well as enhance efficiency. Lastly, Ceglia et al. (2022) mentioned that increasing energy SC (self-consumption) would have greater benefits both from the economic and the environmental perspective since it would reduce the purchase of electricity from the grid and thus mitigate GHG emissions related to non-renewable-based energy systems.

Another advantage of smart energy systems is their resilience against disasters like hurricanes or heatwaves, which can adversely affect electricity generation technologies. Recent studies indicate that the decentralized aspect of smart energy systems enhances their resilience by enabling local electricity generation when centralized power facilities are compromised by disasters such as hurricanes or storms (Hayat, 2016).

Since climate change can lead to more frequent disasters, such as heat waves (Dosio et al., 2018), a crucial component of adaptation is decreasing the impact on electricity generation technology. These studies reveal the potential role of smart energy systems within smart city applications to help in climate change adaptation and mitigation (Obringer and Nateghi, 2021).

In short, smart energy systems remain a viable option to reduce carbon emissions because of their efficiency in terms of energy optimization and because they play a potential role in climate-related disasters.

3.3.4 Smart houses/buildings

One of the greatest technological advances that benefit the environment is the creation of smart houses, buildings, and even residential developments on a greater scale. It should be noted that homes and offices are the two places with the highest energy consumption and significant carbon emissions. In this way, smart homes (Kim and Baek, 2019) have become a great technological goal since they allow the reduction of costs and pollution thanks to interconnected devices that adjust the devices to operate only when needed (see Figure 6) (Froufe et al., 2020; Rawte, 2017).

Figure 6

Figure 6. Image showing some benefits of a smart house. Source: Authors.

3.3.5 Smart agriculture

A significant contributor to global warming is the emission of greenhouse gases (GHGs) from agricultural activities (Lynch et al., 2021). In this way, smart agriculture aims to enhance agricultural efficiency by employing geographic mapping, sensor technology, machine-to-machine connectivity, data analysis, and intelligent information platforms (see Figure 7). The goal is to improve productivity and sustainability in farming practices, leveraging ICTs to ensure food security and resource conservation on a larger scale (Canton, 2021).

Figure 7

Figure 7. Image showing some benefits of smart agriculture. Source: Authors.

ICTs in agriculture, such as e-agriculture, create a collaborative platform involving various stakeholders, particularly farmers, facilitating access to timely information, sharing experiences, and exchanging resources related to agriculture. This approach leverages ICT tools like mobile phones, radio, and television for effective information dissemination, promoting integration with multimedia, knowledge, and cultural sectors (FAO, 2017; Singh et al., 2015).

Digital transformation in agriculture generates valuable data managed through ICT applications and innovations. Technologies like Radio Frequency Identification (RFID) and blockchain enhance data collection, and circulation, and improve traceability in agri-food production, elevating product quality (Braun et al., 2018; Kamilaris et al., 2016; Singh et al., 2015; Tian, 2016; Wolfert et al., 2017).

E-agriculture platforms are still actively used today and continue to evolve, playing an increasingly important role in improving agricultural productivity, sustainability, and food security around the world. FAO e-agriculture Community and Agri-tech Startups are some examples of active e-agriculture.

A study performed by the FAO found that farmers using e-agriculture platforms experienced yield increases of up to 30% compared to those who relied on traditional methods (Food and Agriculture Organization of the United Nations and International Telecommunication Union, 2022). Also, e-agriculture tools that use AI to detect crop diseases have helped farmers take early action, cutting crop losses by 30–40% (Islam et al., 2024).

On the other hand, the environmental benefits of smart agriculture are evident. For example, studies like Maraseni’s et al. (2021) work in Australia demonstrate that optimizing land and water resources can reduce GHG emissions by 45% and increase profitability by over 50% while maintaining emissions 15% lower than the baseline level.

In conclusion, ICTs empower farmers to enhance resource efficiency, productivity, and resilience, thereby reducing food waste in the supply chain and contributing significantly to GHG emissions reduction efforts.

3.3.6 Smart services

In today’s market, a variety of products offering intelligent services are branded with names like smart TVs, smartphones, smart homes, smart energy, and more. These intelligent services are constantly advancing in productivity, compliance, sustainability, and quality, among other aspects, and are evolving alongside various sectors such as government, healthcare, education, finance, hospitality, communications, energy, utilities, and transportation. This evolution is made possible by analytical and cognitive systems like sensing, big data, computation, and automation, enabling smart services to adapt to dynamic environments to benefit customers and suppliers (Lim and Maglio, 2018; Marquardt, 2017).

In Ecuador’s healthcare system, ICT and smart systems have been employed as supportive tools for managing, educating, and preventing trauma (Ordóñez Ríos et al., 2017). Similarly, such systems and applications have been utilized to monitor CO₂ emissions from individuals. For example, the carbon tracking application developed by Svalna App calculates the CO₂ footprint resulting from daily activities. This application offers users information and advice on maintaining acceptable carbon emission levels at work or home, allowing them to set monthly and annual emission targets (Andersson, 2020; Barendregt et al., 2020). Features of the Svalna App are shown in Figure 8.

Figure 8

Figure 8. Images showing three aspects of the Svalna App: an overall display of the user’s greenhouse gas emissions is on the left, a detailed breakdown of emissions across various categories is in the center, and recommendations along with goal-setting options for reducing emissions are on the right (Barendregt et al., 2020), open access.

3.3.7 Smart work/telecommuting

Over the past 30 years, telecommuting has seen a rise, largely due to the integration of ICTs into home and work environments (Barrett, 2001; McCarthy, 2022; OECD, 2019; Standen, 1997). Moreover, the COVID-19 pandemic further accelerated the adoption of telecommuting, resulting in both positive and negative effects on policies, individuals, and the environment (OECD, 2020, 2021; Criscuolo et al., 2021; Manhertz and Lee, 2022; Milasi et al., 2021). The reduction in vehicle usage during lockdown periods led to a decrease in CO₂ emissions (Krasilnikova and Levin-Keitel, 2022). However, the shift to working from home and increased use of electronic devices contributed to a higher carbon footprint. Nevertheless, adopting eco-friendly practices such as unplugging mobile device chargers, maintaining clean email inboxes, and minimizing camera use in video conferences can help reduce the carbon footprint associated with telecommuting. Smart work solutions like video and teleconferencing outside traditional office settings are seen as environmentally sound practices (Obringer et al., 2021; Arnfalk et al., 2020).

Telecommuting also offers various benefits for employees. For instance, it allows for flexible work schedules that can enhance job performance and reduce stress. Additionally, it eliminates the need for parking spaces and office real estate.

In conclusion, telecommuting brings about more positive than negative impacts, and numerous studies view it as a socially, economically, and environmentally beneficial practice for the future.

3.4 Summary of ICT contributions in different sectors

Tables 2–5 provide an overview of how ICT is utilized across various sectors, outlining the benefits and economic/productive outcomes. Additionally, the ICT applications outlined in the tables are analyzed according to the Sustainable Development Goals (SDGs) (Figure 9).

Table 2

Table 2. Use of ICTs in smart agriculture.

Table 3

Table 3. Use of ICTs in smart cities.

Table 4

Table 4. Use of ICTs in smart buildings.

Table 5

Table 5. Use of ICTs in smart transportation.

Figure 9

Figure 9. The Sustainable Development Goals (SDGs) (Setó-Pamies and Papaoikonomou, 2020), open access.

Previous literature has highlighted that key sectors contributing to economic and environmental productivity include smart energy, smart city/building, and smart agriculture. Furthermore, these sectors are analyzed according to Sustainable Development Goals (SDGs) 7, 11, and 12. Here’s a breakdown:

SDG 7 (smart energy): This sector focuses on enhancing energy efficiency and accessibility and promoting renewable energy in the energy mix. Its target is to save over 1.3 billion MWh by 2030.

SDG 11 (smart city and smart buildings): Emphasizes the role of smart buildings and transportation in cities, aiming to reduce CO₂ emissions by 5% by 2030, while also improving resource utilization, energy efficiency, and reducing air pollution.

SDG 12 (smart agriculture): This sector aims to optimize production and consumption patterns, transitioning towards a circular economy model, and targeting a 20% reduction in food waste by 2030.

4 Critical analysis

It has been confirmed that ICT applications play an important role in addressing climate change by improving resource efficiency, supporting informed decision-making, and fostering sustainable practices. However, despite its considerable potential, its implementation brings benefits and challenges that require to be analyzed in detail. In this context, some benefits and limitations are shown in Tables 6, 7.

Table 6

Table 6. Benefits of ICT applications in climate change.

Table 7

Table 7. Limitations of ICT applications in climate change.

5 General discussions

Climate change is one of humanity’s greatest threats due to its tremendous effects on the planet. It emphasizes that greenhouse gases are the primary cause of this climate crisis. In this way, some international organizations such as the United Nations (UN) and others have been making significant efforts to combat these effects and have considered ICTs as an alternative for monitoring and mitigating climate change. Many studies have pointed out the potential of the ICT sector to lower greenhouse gas emissions worldwide. For instance, a study from the Global e-Sustainability Initiative (GeSI) suggests that ICT applications could help avoid around 20% of annual GHG emissions by 2030 through more intelligent energy usage by businesses and consumers (GeSI, 2015). Another study confirms that ICTs can cut global greenhouse gas emissions by up to 15% (10 GtCO₂) by 2030 (Malmodin and Bergmark, 2015). However, the environmental impact of ICTs has not been analyzed in detail. According to Freitag et al. (2021) there are huge trends that can significantly increase the carbon footprint of ICTs, including in AI, IoT, and blockchain. Yu et al. (2024) also analyzed the carbon emissions from 79 prominent AI systems released between 2020 and 2024 and projected that the total carbon footprint from the AI systems in the top 20 carbon emissions could reach up to 102.6 Mt. of CO₂ equivalent per year. In this context, an emissions cap is essential for encouraging industries to adopt greener practices and technologies, paving the way for a more sustainable future for AI.

ICT applications aid in monitoring, forecasting, and managing environmental, ecosystem, and human activities. They offer crucial support to nations in adapting, preparing, and formulating policies for the energy and agricultural sectors, sustainable development, and climate change impacts. Notably, the high energy consumption in urban areas underscores the importance of ICTs in mitigating climate challenges. Energy efficiency in cities is central to the smart city concept and the Internet of Things, leveraging data and interconnected devices via ICT infrastructure (Silva et al., 2018). However, these benefits are counterbalanced by challenges like energy consumption (Kamiya and Bertoldi, 2024), the digital divide (Cooper, 2023), and implementation costs (Freitag et al., 2021). To fully harness the potential of ICTs, efforts should prioritize minimizing its environmental footprint, closing accessibility gaps, and incorporating traditional and community-driven methods. By overcoming these challenges, ICTs can be a more powerful tool in the global effort to combat climate change.

6 Conclusions and perspectives

In recent years, the clear impacts of climate change have been observed through extreme weather conditions, posing threats to public health, infrastructure, and water resources. Nevertheless, integrating Information and Communication Technologies (ICTs) with robust legal frameworks can catalyze the essential changes needed to address global climate challenges. Our literature review has corroborated this, highlighting the pivotal role of ICTs in enabling earth observations and facilitating the exchange of information crucial for decision-making, early warnings, climate tracking, predicting climate changes, managing disasters, and implementing other measures for climate mitigation and adaptation. Consequently, it is recommended that the global community implement specific measures, including ICTs, to tackle climate change and fulfill the Sustainable Development Goals effectively.

Regarding environmental pollution, it is mentioned that ICTs can contribute to reducing at least 20% of global GHG emissions by 2030 from different sectors of the economy (GeSI, 2015). For this, it is recommended that the international community use ICTs in a coordinated, intelligent way to increase productivity and save time and money while reducing the carbon footprint. In other words, using ICTs in a coordinated and intelligent way would require seamless integration of data, collaboration between governments, organizations, and the private sector, standardization of systems, and leveraging cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing. Making this vision possible would involve multilateral cooperation, capacity building in developing countries, and a commitment to secure, open, and sustainable technological deployment.

Concerning ICTs in agriculture (smart agriculture), the digital revolution could drastically transform the face of farms in the coming years. Automation of agricultural activities reduces environmental impact, and production costs and improves animal welfare and food quality.

In the same way, implementing smart cities and energy not only reduces environmental impact and energy consumption but also improves the quality of life and education of residents/populations.

Telecommuting is also shown as a socially, economically, and environmentally beneficial practice for the future because it allows flexible work schedules and consequently improves work performance and reduces stress.

In summary, the evidence shows that ICTs are effective tools for monitoring climate change and provide an alternative to reduce GHG emissions from different sectors in the coming years. Furthermore, it has been proven that ICTs can increase productivity and reduce costs.

Author contributions

FE-T: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing. JZ: Writing – review & editing. FB: Software, Writing – review & editing. MR: Methodology, Writing – review & editing. JP: Conceptualization, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We thank the reviewers for their valuable comments which have allowed us to improve the quality of the article. Additionally, with the help of ChatGPT (Mar 14 version) (large language model), we enhance the quality of some figures and the syntax in the text.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer N-BM declared a shared affiliation with the authors to the handling editor at the time of review.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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