Drivers Of Emissions Research Articles

Analysis of Energy-Related-CO2-Emission Decoupling from Economic Expansion and CO2 Drivers: The Tianjin Experience in China

Cities are key areas for carbon control and reduction. The study of the decoupling between CO2 emissions and gross domestic product (GDP) and the drivers of CO2 emissions in cities facilitates the reduction of CO2 emissions to safeguard the development of the economy. This paper first calculates the CO2 emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO2-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO2 emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO2 emissions, it is essential to harmonize the interplay between economic-development approach and CO2 emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO2 emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO2 emission factors and can also interest urban policymakers.

Greenhouse gas emissions from urban river waters of China's major cities

Urban rivers are emerging as potent emitters of greenhouse gases (GHGs). However, the magnitude and drivers of GHG emissions from urban rivers are not well understood at a national level. Using a quantitative synthesis of 4633 data records collected from urban river waters of 23 major Chinese cities, this study delved into a systematic analysis of GHG emissions from urban rivers across China. The study showed that GHG emissions in these urban rivers varied spatially and temporally, with the highest carbon emission (CO2 + CH4) from the rivers draining large metropolitan areas and during the summertime. CO2, CH4, and N2O concentrations in the urban river waters ranged from 22.53 to 1052.20 (188.41 ± 223.10) μmolL−1, 0.02 to 21.32 (1.35 ± 1.59) μmolL−1, and 0.01 to 16.81 (0.21 ± 0.72) μmolL−1, respectively, with an average flux of 148.98 ± 245.30 mmolm−2d−1 for CO2, 9.41 ± 25.08 mmolm−2d−1 for CH4, and 0.19 ± 0.65 mmolm−2d−1 for N2O. Urban rivers also received large domestic sewage with high nutrient and carbon loadings due to huge populations, indicating the effect of wastewater treatment plant effluent and urban runoff on GHG production. These findings imply that direct GHG contributions from rivers in urban areas should be accurately quantified and consequently be included in GHG inventories of riverine GHG emissions.

Historical Evolution and Current Developments in Building Thermal Insulation Materials—A Review

The European Climate Law mandates a 55% reduction in CO2 emissions by 2030, intending to achieve climate neutrality by 2050. To meet these targets, there is a strong focus on reducing energy consumption in buildings, particularly for heating and cooling, which are the primary drivers of energy use and greenhouse gas emissions. As a result, the demand for energy-efficient and sustainable buildings is increasing, and thermal insulation plays a crucial role in minimizing energy consumption for both winter heating and summer cooling. This review explores the historical development of thermal insulation materials, beginning with natural options such as straw, wool, and clay, progressing to materials like cork, asbestos, and mineral wool, and culminating in synthetic insulators such as fiberglass and polystyrene. The review also examines innovative materials like polyurethane foam, vacuum insulation panels, and cement foams enhanced with phase change materials. Additionally, it highlights the renewed interest in environmentally friendly materials like cellulose, hemp, and sheep wool. The current challenges in developing sustainable, high-performance building solutions are discussed, including the implementation of the 6R principles for insulating materials. Finally, the review not only traces the historical evolution of insulation materials but also provides various classifications and summarizes emerging aspects in the field.

Unveiling the nonlinear drivers of urban land resources on carbon emissions: The mediating role of industrial upgrading and technological innovation

As cities continue to develop, land resources play a pivotal role in the pursuit of sustainable urban development. However, the impact and mechanisms of land resources on reducing emissions remain unclear. Therefore, our study employs an ensemble learning model within a Monte Carlo Simulation framework to explore the nonlinear relationship between land resources and carbon emissions. Additionally, we utilize a fixed effects model to identify the mechanisms of impact. Specifically, industrial and residential land uses are identified as primary drivers of carbon emissions, contributing 17.04 % and 4.75 % of the total variance in emissions, respectively. In contrast, a standard deviation increase in land price leads to a reduction of 4.65 tons in per capita carbon emissions. In addition, industrial upgrading and innovation emerge as critical mediators in promoting carbon reduction. This research contributes to sustainable urban planning and policy-making, offering insights into how strategic land use planning reduces urban carbon footprints.

Material efficiency at the component level: how much metal can we do without?

Global production of steel and aluminium is a major driver of greenhouse gas emissions. Various processes might allow continued primary production of the two metals, but all depend on emissions-free electricity or carbon storage, and global capacity of these two key resources will be below demand for decades to come. As a result, zero-emissions steel and aluminium will mainly come from recycling, but supply will be lower than demand. This motivates demand reduction, and for the first time, this article estimates the inefficiency in current metal use by component type. The results demonstrate that around 80% of steel and 90% of aluminium liquid metal produced today may be unnecessary. Around 40% of liquid steel and 60% of liquid aluminium are never used in final components as they are removed along the supply chain of manufacturing. Of the metal that enters final service, approximately one-third could be saved by avoiding component over-specification. A further third could be saved, where the properties of metal are not used to their limits. These results point to specific opportunities for innovation in design and manufacturing technology, of which the highest priority is to re-think the use of sheet metal in construction.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Monitoring of Greenhouse Gas Emission Drivers in Atlantic Canadian Potato Production: A Robust Explainable Intelligent Glass-Box

In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Maritime potato crop system i.e., Carbon dioxide (CO2), nitrous oxide (N2O), and water vapour (H2O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological and soil properties information was collected from three Canadian sites, two in Prince Edward Island (PEI) and one in New Brunswick. This advanced framework includes a transparent multi-level pre-processing module and a Runge-Kutta optimizer (RUN), integrated with an eXplainable gradient-boosted decision Tree (GBDT) machine learning (ML) technique. The preprocessing scheme meticulously selects the most effective input combinations from the hydro-meteorological and soil properties datasets using hybridization of Boruta-GBDT for feature selection, Best Subset Least Absolute Shrinkage and Selection Operator (BSLR), and Weighted Aggregated Sum Product Assessment (WASPAS). The optimal combinations were then analyzed using the GBDT-RUN and compared against two algorithms: LightGBM coupled with RUN optimizer (LightGBM-RUN) and classical GBDT. The explainability of the primary model was enhanced using SHapley Additive exPlanations (SHAP). Model validation employed various metrics, such as the correlation coefficient (R), squared deviation (SquD), and a range of sophisticated statistical graphics. Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO2|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; N2O|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; H2O| R=0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. Furthermore, the explainability analysis identified dew point and soil temperature as the most influential factors in the CO2, N2O, and H2O emissions scenarios.

Effect of alternative dosing strategies of pembrolizumab and nivolumab on health-care emissions in the Netherlands: a carbon footprint analysis

Hospitals contribute substantially to greenhouse gas emissions and face a moral obligation to prioritise emission reduction. Drugs constitute an important component of the greenhouse gas emissions of hospitals. Alternative dosing strategies (ADS) have been implemented to improve the cost-effectiveness of pembrolizumab and nivolumab. However, the impact of these ADS on greenhouse gas emissions remains unknown. Therefore, we aimed to analyse the effect of ADS implementation on the carbon emissions of treatment with pembrolizumab and nivolumab. We used a process-based lifecycle assessment to quantify the environmental impact of pembrolizumab and nivolumab, focused on equivalent carbon dioxide emissions (CO2e). Lifecycle inventory and impact data from Erasmus University Medical Center (Rotterdam, Netherlands) were used to calculate the CO2e for pembrolizumab and nivolumab, their dosing intervals, and the impact of ADS on CO2e. The functional unit of the study was the administration of a single dose of pembrolizumab or nivolumab. In 2022, the annual carbon emissions related to pembrolizumab and nivolumab treatment in the Erasmus University Medical Center were 445 tons of CO2e, averaging 94 kg of CO2e per dose. Pharmaceutical production was the main driver of treatment-related carbon emissions (mean 92·9% of total emissions). Applying ADS resulted in 21-26% and 9-11% CO2e reductions for pembrolizumab and nivolumab, respectively. This study shows the environmental impact of pembrolizumab and nivolumab treatment and calls for further implementation of ADS for pembrolizumab, nivolumab, and other anti-PD-(L)1 monoclonal antibodies, and more sustainable pharmaceutical production processes. Our findings create environmental awareness and contribute to the promotion and understanding of health-care practices with lower carbon emissions. None.

Quantifying Drivers of GHG Emissions in ASEAN: Modeling CO2 Emissions Using LMDI and ARIMAX Approaches

Quantifying Drivers of GHG Emissions in ASEAN: Modeling CO2 Emissions Using LMDI and ARIMAX Approaches

Mapping the composite institutional dynamics risks and spatial spillover effects on environmental sustainability in 28 Asian economies

This study emphasizes the impact of economic, financial, and political institutions on environmental sustainability across 28 Asian countries from 1995 to 2022. Using a spatial Durbin model, we analyzed the direct effects of these institutional factors within each country as well as their spatial spillover effects on neighboring nations. Our findings demonstrate the interconnectedness of environmental risks and the broader regional implications. This highlights the critical need for collaborative policy initiatives at the regional level to promote environmental sustainability. The results provide important insights for policymakers and international organizations seeking to address environmental sustainability in an increasingly globalized context. By emphasizing the role of institutions and spatial interdependencies, this research offers a nuanced understanding of the drivers of emissions in Asia and the potential for coordinated action to mitigate climate change.

Do digitalization and green innovation limit carbon emissions? Evidences from BRICS economies

Rapidly evolving innovation and digitalization have captured the focus of policymakers and scholars regarding their potent role in influencing environmental quality. The present research analyzes the impact of these variables on the carbon emissions of Brazil, Russia, India, China, and South Africa economies from 1990 to 2021. This research also explores the impact of economic growth, quadratic green innovation, and green energy on carbon emissions. Using several panel diagnostic tests, this research validates heterogeneous slopes, the presence of cross-sectional dependence, and significant cointegration. Due to the mixed integration order, this research uses a cross-sectional augmented autoregressive distributed lag model, and the results show that economic expansion and green innovation are significant drivers of emissions in both the short and long run. However, digitalization, quadratic green innovation, environmental policy stringency, and green energy are significant in improving environmental quality and sustainability. The long-term results are tested by employing a series of parametric and nonparametric regressions. This research recommends further investment in environmental research and development, digital technologies, green innovation, and the strengthening of environmental policies to attain sustainable development.

Drivers of greenhouse gas emissions in agricultural soils: the effect of residue management and soil type

Developing successful mitigation strategies for greenhouse gases (GHGs) from crop residue returned to the soil can be difficult due to an incomplete understanding of factors controlling their magnitude and direction. Therefore, this study investigates the effects of varying levels of wheat residue (WR) and nutrient management on GHGs emissions (CO2, N2O, and CH4) across three soil types: Alfisol, Vertisol, and Inceptisol. A combination of laboratory-based measurements and a variety of data analysis techniques was used to assess the GHG responses under four levels of WR inputs (0, 5, 10, and 15 Mg/ha; WR0, WR5, WR10, and WR15) and three levels of nutrient (NP0: no nutrient, NP1: nutrients (N and P) were added to balance the residue C/nutrient stoichiometry of C/N/P= 100: 8.3: 2.0 to achieve 30% stabilization of added residue C input at 5 Mg/ha (R5), and NP2: 3 × NP1). The results of this study clearly showed that averaged across residue and nutrient input, Inceptisol showed negative N2O flux, suggesting consumption which was supported by its high legacy phosphorus (19.7 mg kg⁻1), elevated pH (8.49), and lower clay content (13%), which reduced microbial activity, as indicated by lower microbial biomass carbon (MBC) and alkaline phosphatase (Alk-P) levels. N2O emissions were more responsive to nutrient inputs, particularly in Vertisol under high WR (15 Mg/ha) input, while CH4 fluxes were significantly reduced under high residue inputs, especially in Vertisol and Inceptisol. Alfisol exhibited the highest total carbon mineralization and GWP, with cumulative GWP being 1.2 times higher than Vertisol and 1.4 times higher than Inceptisol across residue and nutrient input. The partial least square (PLS) regression revealed that anthropogenic factors significantly influenced CO2 and N2O fluxes more than CH4. The anthropogenic drivers contributed 62% and 44% of the variance explained for N2O and CH4 responses. Our study proves that different biogeochemical mechanisms operate simultaneously depending on the stoichiometry of residue C and nutrients influencing soil GHG responses. Our findings provide insight into the relative contribution of anthropogenic and natural drivers to agricultural GHG emissions, which are relevant for developing process-based models and addressing the broader challenge of climate change mitigation through crop residue management.

Shift from legacy to emerging per- and polyfluoroalkyl substances for watershed management along the coast of China

Per- and polyfluoroalkyl substances and their short-chain alternatives have attracted world-wide attention due to their widespread presence and persistence in the environment. However, the sources, environmental fate, and driving forces of PFAS in coastal ecosystems remain poorly understood. In this study, the spatial distribution, source apportionment, and driving mechanisms of PFAS were investigated through a comprehensive analysis of water samples collected along the China's coastline. The concentrations of Σ25PFAS in water samples followed a general pattern, with higher levels observed in northern coastal zones of China than the south, ranging from 0.72 to 1872.21 ng L−1. PFOA and PFBA were dominant. Emerging short-chain PFAS, such as PFBS, PFBA, F-53B and GenX, were frequently detected, with detection rates of 97%, 99%, 95% and 77%, respectively. This indicated a shift in coastal PFAS contamination from legacy compounds to emerging short-chain alternatives. Source apportionment using the Positive Matrix Factorization model identified key contributors to PFAS pollution, including textile production, volatile precursors, precious metal industries, aqueous film-forming foam, metal-plating, electrochemical fluorination, and fluoropolymer manufacturing. Additionally, PFAS concentrations were significantly positively correlated with cultivated land, urban area, and wastewater discharge, while negatively correlated with annual precipitation and woodland coverage (p < 0.05). Socio-economic development was identified as a major driver of PFAS emissions, while the hydrological factors and vegetation coverage can significantly enhance watershed resilience against PFAS pollution.

Patterns and Drivers of Greenhouse Gas Emissions in a Tropical Rubber Plantation from Hainan, Danzhou

The intensification of global climate change has made the study of greenhouse gas (GHG) emissions increasingly important. To gain a deeper understanding of the emission characteristics and driving factors of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) from rubber plantation soils, this study conducted a 16-month continuous observation in a rubber plantation in Danzhou, Hainan, employing the static chamber method for the monthly sampling and measurement of GHG emissions while analyzing the soil’s physical and chemical properties. The results indicated that the N2O flux exhibited no significant diurnal variation between the dry and rainy seasons, with an average emission rate of 0.03 ± 0.002 mg·m−2·h−1. A clear seasonal trend was observed, with higher emissions in summer than in winter, resulting in an annual flux of 3 kg·hm−2·a−1 (equivalent to 1.9 kg N·hm−2·a−1). N2O emissions were significantly correlated with soil temperature and moisture, explaining 46% and 40% of the variations, respectively, while soil ammonium nitrogen content also significantly influenced N2O and CO2 emissions. The rubber plantation soil acted as a source of N2O and CO2 emissions and a sink for CH2, with lower emissions of N2O and CO2 during the daytime compared to nighttime, and higher CH4 uptake during the daytime. In the dry season, there was a significant positive correlation between N2O and CO2 emissions (R2 = 0.74, p &lt; 0.001). This study reveals the diurnal and seasonal patterns of GHG emissions from rubber plantation soils in Hainan and their interrelationships, providing a scientific basis for the low-carbon management of rubber plantations and GHG mitigation strategies, thereby contributing to attempts to reduce the impact of rubber cultivation on climate change.

Dispersal of microbes from grassland fire smoke to soils.

Wildland fire is increasingly recognized as a driver of bioaerosol emissions, but the effects that smoke-emitted microbes have on the diversity and community assembly patterns of the habitats where they are deposited remain unknown. In this study, we examined whether microbes aerosolized by biomass burning smoke detectably impact the composition and function of soil sinks using lab-based mesocosm experiments. Soils either containing the native microbial community or presterilized by γ-irradiation were inundated with various doses of smoke from native tallgrass prairie grasses. Smoke-inundated, γ-irradiated soils exhibited significantly higher respiration rates than both smoke-inundated, native soils and γ-irradiated soils exposed to ambient air only. Microbial communities in γ-irradiated soils were significantly different between smoke-treated and control soils, which supports the hypothesis that wildland fire smoke can act as a dispersal agent. Community compositions differed based on smoke dose, incubation time, and soil type. Concentrations of phosphate and microbial biomass carbon and nitrogen together with pH were significant predictors of community composition. Source tracking analysis attributed smoke as contributing nearly 30% of the taxa found in smoke-inundated, γ-irradiated soils, suggesting smoke may play a role in the recovery of microbial communities in similar damaged soils. Our findings demonstrate that short-distance microbial dispersal by biomass burning smoke can influence the assembly processes of microbial communities in soils and has implications for a broad range of fields including agriculture, restoration, plant disease, and biodiversity.

Decomposition of Carbon Dioxide (CO2) Emissions in Hungary

Elevated levels of global greenhouse gases (GHGs), primarily produced through fossil fuel combustion, present environmental risks such as prolonged droughts, global warming, and catastrophic floods. Despite raising awareness and international efforts to combat climate change, fossil fuel and GHGs demand persists. At this juncture, this study investigates Hungary's transportation sector, a notable CO2 emitter. Using the Kaya Identity and Log Mean Divisia Index method, this study decomposes the primary components of CO2 emissions in Hungary's transport sectors from 2001-2021. This analysis is done in two decomposition levels, annual and periodic decomposition analysis for each of the five years starting from 2001. The analysis reveals that the economic activity effect ∆CGPE is the predominant driver of CO2 emissions, while the energy intensity effect ∆CEIE improvements have substantially mitigated these increases. Population changes affect ∆CPOPE, and the carbon emission intensity effect ∆CCEIE has had minimal impacts. The study provides insights and recommendations for policy and strategic interventions to reduce emissions and promote sustainable transportation practices in Hungary.

A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities

As the world’s largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China’s carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

Aquaculture growth and coastal tourism development in the context sustainable blue economy

AbstractThe primary objective of this research is to investigate the impact of increased aquaculture output on coastal tourism and blue growth in European Union developed countries (EU13) and European Union developing countries (EU14) from 1990 to 2023. Key findings were derived through the application of resilient least squares (RLS), two‐stage least squares (2SLS), and ordinary least squares (OLS) estimators, effectively addressing endogeneity concerns. The exact magnitudes of these results show that, in EU14 developed countries, the increase of aquaculture output seriously jeopardizes the sustainability of coastal tourism more so than in EU13 developing countries. In contrast, EU14 developed countries find that variables like as economic growth and institutional quality have a greater impact on improving coastal sustainable development than do EU13 countries. Moreover, the data shows that, in comparison to EU14 developed countries, the aquaculture sector in EU13 developing countries uses more fossil fuels than EU14 developed countries, which is the main driver of carbon dioxide emissions in coastal tourist areas. In light of these findings, the study recommends that policymakers give policies targeted at enhancing productivity and encouraging the environmentally friendly growth of aquaculture production top priority, particularly in the EU14 developed countries that have significant negative effects on the sustainable development of coastal tourism.

Balancing growth and preservation: Unravelling Africa's carbon-economic nexus through the environmental kuznets curve

Africa is vulnerable to the challenges of global climate change, which poses a significant obstacle to achieving economic development while protecting the environment. Despite numerous Environmental Kuznets Curve (EKC) studies, evidence remains inconclusive, especially in African countries. This study delves into the carbon-economic nexus within the EKC framework across 46 African countries from 1990 to 2020. Utilizing advanced econometric techniques such as Auto-Regressive Distributed Lag (ARDL) modeling based on the Pooled Mean Group (PMG) estimator, Common Correlated Effects Pooled Mean Group (CCE-PMG), appropriate U test, and Dumitrescu and Hurlin causality test, the research uncovers significant insights into how GDP, energy consumption, and urbanization impact CO2 emissions across different income groups. The findings strongly support the EKC hypothesis for lower-middle-income (LMI) and upper-middle- and high-income (UM&HI) countries, where economic growth initially worsens environmental degradation but eventually leads to improvements as incomes rise. However, in low-income (LI) countries, the study reveals a U-shaped relationship, suggesting that early economic growth may reduce emissions, but continued development could lead to increased environmental stress. Energy consumption is identified as a persistent driver of CO2 emissions across all income groups, while urbanization shows mixed impacts, with negative environmental consequences in LI countries and potential environmental benefits in LMI and UM&HI countries. These findings carry important policy implications for African countries as they seek to balance economic growth with environmental preservation. The research calls for targeted policies that promote green technologies, enhance energy efficiency, and implement sustainable urban planning to ensure that Africa's development trajectory is environmentally sustainable.

Revealing the Local Time Structure of the Alfvén Radius and Travel Times in Jupiter's Magnetosphere

AbstractJovian magnetospheric plasma is coupled to the ionosphere through Alfvén waves. Alfvén waves enable the transport of angular momentum and energy between the planet and magnetospheric plasma, a process that ultimately generates Jupiter's bright auroral emissions. However, past the Alfvén radius, the location where the radial velocity is greater than the Alfvén velocity, magnetospheric plasma is decoupled from the planet. Alfvén waves launched by magnetospheric plasma do not reach the ionosphere, angular momentum cannot be transported from the planet, and auroral emissions should diminish. Knowledge of Jupiter's Alfvén radius location is critical for interpreting drivers of auroral emissions, in situ data, and applications of numerical models. Previous studies that determined the location of the Alfvén radius assumed an azimuthally symmetric magnetosphere and local‐time independent magnetic field. Here, we employ a statistical description of the magnetic field that includes local time effects. We find a minimum Alfvén radius of 30 (Jupiter radii) at 6 LT, with plasma decoupled from the planet in the post‐dusk through dawn sector. Furthermore, no Alfvén radius exists within 60 between 8 and 20 LT. At distances greater than 50 , the Alfvén travel time is such that magnetospheric plasma moves substantially in the magnetosphere before angular momentum can be efficiently transferred from the atmosphere. Therefore, the angular momentum supplied may no longer be sufficient for the local conditions. Our results highlight the importance of local time considerations and offer new interpretations for local time dependent auroral features, such as the polar collar.

Assessing the energy system's greenhouse emissions via the health of Smart Territories and Cities

The aim of this paper is to understand how the main drivers of air pollution and greenhouse emissions impact public health in a Smart Territory — meaning a technologically integrated city or neighborhood — as well as how to design an optimized energy system model beyond only data by including the holistic experience of its people through phenomenology and ethics. We understand that a Territory and a City is a complex system where mathematical tool modeling known as Design of Experiment (DOE) and its optimization solutions are required to establish causality and identify the variables that have a broader impact on public health so that mortality rates due to air pollution are reduced. DOE's statistical branch is a novel methodology when applied to energy systems and the design of Smart Territories and Cities. Because of it, we have been able to demonstrate how energy emissions, capacity and Levelized Cost of Energy (LCOE) affect the mortality rate and create the foundations for building fully decarbonised energy systems enhanced by mathematical solutions. However, the Cartesian approach of extrapolating from models which align themselves with economic growth alone will not solve the energy dilemma. In fact, the current energy transition project is based on outdated archetypes that lead to scary futures. To course-correct the problematic energy model of a Smart Territory, phenomenologists and creative imagination need to be cultivated and put into action together with mathematical modeling, leading to vectors of positive change and adaptation. Only then might the energy models designed to be reproducible could became ethically virtuous and therefore accepted by the Territory's co-dwellers within the chain of causalities.