A global prospective of environmental degradations: economy and finance

Finance plays a crucial role in a fast-growing economy that can lead to environmental degradation. The present study utilizes balanced panel data of 105 countries for the time span 1980–2016 to investigate empirical linkage among environmental degradations: economy and finance. It also unfolds the nonlinear impact of economy and finance on environmental degradation. Existing literature on environmental issues mainly focuses on individual case studies uncovering particular regions, but the comprehensive analysis is not available. To fill this gap, panels were classified into five divisions: global, regional, income-based, OECD-based, and carbon emission. The cross-sectional dependence test is applied to identify the degree of cross-sectional dependence among concerned 16 divisions. The second-generation panel models (CADF and Westerlund cointegration, DOLS, and DH heterogenous causality) are employed on a sample set to compute to unit root, cointegration, and long-run and short-run dynamics among concerned variables, respectively. The findings infer the inverted EKC and U-shaped EKC in 10 and 3 out of 16 divisions with respect to environmental degradation—economy nexus, respectively, while 8 and 2 out of 16 divisions indicate the inverted EKC and U-shaped EKC, respectively, in terms of environmental degradation—finance nexus. In 12 out of 16 divisions, the energy consumption uplifts the CO₂ emissions. The DH causality affirmed a bidirectional causality among economy, finance, and energy consumption, respectively.

     

Economic complexity,ICT, biomass energy consumption,and environmental degradation: evidence from Iran

Economic complexity, biomass energy consumption, and information communication technology (ICT) have diverse impacts on energy consumption and carbon dioxide (CO₂) emissions. Nevertheless, analysis of these variable effects is not addressed in the previous literature; the antiqueness of this article is stuffing this gap. This study assessed the relationship between gross domestic product (GDP) per capita, biomass consumption, economic complexity index (ECI), ICT, and CO₂ emissions in Iran in 1994–2018. The autoregressive distributed lag (ARDL) model and the quantile regression (QR) econometric technique were used to investigate the factors affecting CO₂ emissions in the tails of the conditional distribution. The share of each influential factor was predicted through the variance decomposition analysis (VD) for the next 10 years. The empirical results showed a long-run relationship between the variables. So, the variables of biomass consumption, ECI, and ICT improve the quality of the environment in Iran by reducing CO₂ emissions, and the per capita GDP variable increases CO₂ emissions. Results suggest no evidence indicating the presence of environmental Kuznets curve (EKC); however, QR demonstrated the existence of EKCs in the lower quantiles of the conditional distribution. The ECI will have the most share to change the CO₂ emissions in the future. The income threshold should be determined at the turning point of the EKC to increase economic development. Moreover, investing in increasing biomass consumption is vital. Policymakers also need to consider strict added value for the export of products.

     

The effects of economic growth and innovation on CO2 emissions in different regions

Economic growth and economic energy consumption have received greater attention due to its contribution to global CO₂ emissions in recent decades. The literature on CO₂ emissions and innovation for regional differences is very scanty as there is not enough study that considered different regions in a single analysis. We adopt a holistic approach by incorporating different regions so as to assess how innovation contributes to emission reduction. The study, therefore, examined the effects of innovation and economic growth on CO₂ emissions for 18 developed and developing countries over the period of 1990 to 2016. The study used panel technique capable of dealing with cross-section dependence effects: panel cross-sectional augmented Dickey-Fuller (CADF) unit root to determine the order of integration, Westerlund cointegration tests confirmed that the variables are co-integrated. We employed panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) to estimate the long-run relationship. The results show that energy consumption increases CO₂ emissions at all panel levels. However, innovation reduces CO₂ emissions in G6 while it increases emissions in the MENA and the BRICS countries. Environmental Kuznets curve (EKC) hypothesis is valid for the BRICS. The pollution haven hypothesis (PHH) and pollution halo effect were confirmed at different panel levels. Based on the findings different policy recommendations are proposed.

     

Does agriculture-induced environmental Kuznets curve exist in developing countries?

Even though the economic growth and the increased agricultural production bring welfare increment, they could also load significant environmental costs to society. This study aims to investigate the existence of agriculture-induced EKC hypothesis in 47 developing countries during 1976–2017 by using dynamic panel data estimators. According to the long-run findings of the DOLS and the FMOLS estimators, there is an inverted U-shaped relationship that exists between income and the CO₂ emissions, while agricultural production has a negative impact on environmental quality. In addition, the PMG estimator is also employed for robustness check. Likewise the DOLS and the FMOLS findings, the long-run results of the PMG estimator also support the existence of agriculture-induced EKC for developing countries. The empirical findings of this study provide stimulus results of policy makers to re-consider their current production infrastructure of agricultural sector to achieve sustainable development.

     

How to bend down the environmental Kuznets curve: the significance of biomass energy

Sources of renewable energy have received wide attention in the literature because of serious threats to the environment. However, some renewable resources, including biomass energy role is debatable in the energy economics literature. This empirical work focuses to analyze the role of biomass energy in carbon dioxide (CO₂) emissions using the framework of the environmental Kuznets curve (EKC) in Pakistan over the period from 1980 to 2015. The bound testing approach suggests there is cointegration among study variables. The study uses an auto-regressive distributed lag model (ARDL) with a structural break in the series. To summarize the findings of the study, it can be inferred that biomass energy increase CO₂ emissions. In addition, biomass energy helps to form a U-shaped relationship between income and CO₂ emissions that support the EKC hypothesis. Also, the feedback hypothesis is found between biomass energy and CO₂ emissions. The findings would guide policymaker with practical guidelines to formulate policies to utilize a high amount of biomass energy in a sustainable manner.

     

Roadmap for climate alliance economies to vision 2030: retrospect and lessons

The United Nations Climate Conference 25, held in December 2019, reached a significant agreement against implementing the Paris agreement come 2020. Bound by the contract, 189 countries who are party to the deal agreed to constrain worldwide temperature to ascend to 1.5° Celsius. To this end, the present study attempts to investigate the readiness of selected countries in the European Union to implement the agreement, which will better the quality of the global environment. In line with this, this study appraises the connection between economic growth, renewable and non-renewable energy consumption, on emissions in 11 countries in the European Union from 1990 to 2016. The study utilises the Pooled Mean Group-Auto Regressive Distributed Lag (PMG-ARDL) model estimator and Dumitrescu and Hurlin Panel Causality analysis to analyse the long-run and short-run impact and direction of causality among these factors, respectively. The long-run study's empirical results show a U-shaped Environmental Kuznets Curve (EKC) and a negative connection between renewable energy use and emissions in the EU-11 countries. In the short-run, non-renewable energy use worsens CO₂ emissions while renewable energy use leads to a fall in emissions. Similarly, causality tests show a feedback mechanism between emissions and renewable energy use and between non-renewable energy and renewable use. Also, there is unidirectional causality from income to CO₂ emissions, non-renewable energy use to CO₂ emissions. The investigation recommends an expanded proportion of renewable energy sources in the EU countries’ energy mix to cut down on emissions.

     

Effects of urbanization and nonrenewable energy on carbon emission in Africa

This study investigates the impact of urbanization and nonrenewable energy consumption on carbon emissions. The context of the analysis is 54 African Union countries from 1996 to 2019. For estimation, we use panel quantile regression (PQR) and fully modified ordinary least squares (FMOLS). Our regression results demonstrate that there is a positive correlation between urbanization and CO₂ emission. Further, our empirical results confirmed that nonrenewable energy consumption increases environmental pollution in African Union countries. The outcomes demonstrate the EKC hypothesis because at the initial stage of development, when economic growth increases, environmental pollution increases; after a threshold point, environmental pollution decreases as economic growth increases. It can find an inverted U-shaped relationship between economic growth and CO₂ emission. The findings also show that urbanization should be planned; otherwise, it can lead to environmental degradation in the long run. Africa continent takes strict action and builds a blueprint for efficient and effective energy production and consumption. The only solution to achieve green growth in Africa is to shift from fossil fuel energy supply to renewable energy supply.

     

Carbon dioxide emissions,output, and energy consumption categories in Algeria

This study examines the relation between CO₂ emissions, income, non-renewable, and renewable energy consumption in Algeria during the period extending from 1980 to 2011. Our work gives particular attention to the validity of environmental Kuznets curve (EKC) hypothesis. The autoregressive distributed lag (ARDL) with break point method outcome demonstrates the positive effect of non-renewable type of energy on CO₂ emissions consumption. On the contrary, the results reveal an insignificant effect of renewable energy on environment improvement. Moreover, the results accept the existence of EKC hypothesis but the highest gross domestic product value in logarithm scale of our data is inferior to the estimated turning point. Consequently, policy-makers in Algeria should expand the ratio of renewable energy and should decrease the quota of non-renewable energy consumption.     

Do renewable energies contribute to enhancing environmental quality in Eastern Africa?

East Africa has enormous renewable energy potential, but only a small portion of it has been exploited, and little is known on its role in improving environmental quality. Thus, this study empirically examines the impact of renewable energy on the environment using ecological footprint (EF; positive indicator) and CO₂ emissions (negative indicator) as proxy indicators for environmental quality in a panel of ten East African countries from 1990 to 2015. These indicators were chosen due to their potential impact in the environment. The work used the pooled mean group (PMG) as the main panel estimator to determine the impact while controlling non-renewable energy consumption, GDP per capita, and foreign direct investment (FDI). PMG has been used as it forces the long-run coefficients to be equal across all panel groups. The findings show that in the long run, there is a significant negative relationship between CO₂ emissions and renewable energy consumption, as well as a significant positive relationship (with a low impact) between EF and renewable energy consumption, suggesting that renewable energy use enhances the area’s environmental quality. Also, results indicate that non-renewable energy use degrades environmental quality in both metrics, whereas GDP degrades environmental quality through CO₂ emissions and improves environmental quality through EF. This requires East African countries to focus a higher emphasis on accessible renewable energy sources to achieve quick and sustainable economic growth and minimize environmental effects. To accomplish this, strategic policies and legislation, as well as the promotion of green technology, are required.

     

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

     

Do transportation taxes promote pro-environmental behaviour? An empirical investigation

The transportation sector is a crucial driver of energy intensity and environmental degradation. Therefore, we aim to explore the nexus of transportation taxes, energy intensity, and CO₂ emissions for the BICS economies. The econometric approaches, CS-ARDL and PMG-ARDL, have been employed to compute the estimates. The long-run estimates of the green transportation tax variable are negatively significant in both energy intensity and CO₂ emissions models irrespective of the estimation technique. These findings imply that green transportation taxes help reduce energy intensity and CO₂ emissions in BICS economies. Conversely, in the short-run, the effects of transportation taxes on energy intensity and CO₂ emissions are mixed and inconclusive. Hence, transportation taxes are necessary to keep the polluters under control not only from the transport sector but also serve as a deterrent for other sectors as well.

     

Regional and sectoral assessment of greenhouse gas emissions in India

In this paper the authors have estimated for 1990 and 1995 the inventory of greenhouse gases CO₂, CH₄ and N₂O for India at a national and sub-regional district level. The district level estimates are important for improving the national inventories as well as for developing sound mitigation strategies at manageable smaller scales. Our estimates indicate that the total CO₂, CH₄ and N₂O emissions from India were 592.5, 17, 0.2 and 778, 18, 0.3 Tg in 1990 and 1995, respectively. The compounded annual growth rate (CAGR) of these gases over this period were 6.3, 1.2 and 3.3%, respectively. The districts have been ranked according to their order of emissions and the relatively large emitters are termed as hotspots. A direct correlation between coal consumption and districts with high CO₂ emission was observed. CO₂ emission from the largest 10% emitters increased by 8.1% in 1995 with respect to 1990 and emissions from rest of the districts decreased over the same period, thereby indicating a skewed primary energy consumption pattern for the country. Livestock followed by rice cultivation were the dominant CH₄ emitting sources. The waste sector though a large CH₄ emitter in the developed countries, only contributed about 10% the total CH₄ emission from all sources as most of the waste generated in India is allowed to decompose aerobically. N₂O emissions from the use of nitrogen fertilizer were maximum in both the years (more than 60% of the total N₂O). High emission intensities, in terms of CO₂ equivalent, are in districts of Gangetic plains, delta areas, and the southern part of the country. These overlap with districts with large coal mines, mega power plants, intensive paddy cultivation and high fertilizer use. The study indicates that the 25 highest emitting districts account for more than 37% of all India CO₂ equivalent GHG emissions. Electric power generation has emerged as the dominant source of GHG emissions, followed by emissions from steel and cement plants. It is therefore suggested, to target for GHG mitigation, the 40 largest coal-based thermal plants, five largest steel plants and 15 largest cement plants in India as the first step.     

Does energy consumption contribute to environmental pollutants? evidence from SAARC countries

The objective of the study is to examine the causal relationship between energy consumption and environmental pollutants in selected South Asian Association for Regional Cooperation (SAARC) countries, namely, Bangladesh, India, Nepal, Pakistan, and Srilanka, over the period of 1975–2011. The results indicate that energy consumption acts as an important driver to increase environmental pollutants in SAARC countries. Granger causality runs from energy consumption to environmental pollutants, but not vice versa, except carbon dioxide (CO₂) emissions in Nepal where there exists a bidirectional causality between CO₂ and energy consumption. Methane emissions in Bangladesh, Pakistan, and Srilanka and extreme temperature in India and Srilanka do not Granger cause energy consumption via both routes, which holds neutrality hypothesis. Variance decomposition analysis shows that among all the environmental indicators, CO₂ in Bangladesh and Nepal exerts the largest contribution to changes in electric power consumption. Average precipitation in India, methane emissions in Pakistan, and extreme temperature in Srilanka exert the largest contribution.     

Influence of tourism,governance, and foreign direct investment on energy consumption and CO2 emissions: a panel analysis of Muslim countries

The empirical linkages from tourism, governance, and FDI have been quantified on CO₂ emission and energy use over 2002–2014 for a panel of 13 Muslim countries. To this end, we have examined the data for cross-sectional dependence (CD) and panel heterogeneity and employed panel algorithms, which account for both CD and panel heterogeneity. The results from Pedroni, Westerlund, and Kao tests supported the existence of a cointegration association between the chosen variables. In the CO₂ model, we observed that tourism positively, and governance negatively, influences the CO₂ emission. However, in the case of the energy model, the results of tourism pose a negative relationship, and governance indicates a positive relationship with energy use. The results supported the pollution haven phenomenon, finance, and energy triggered pollution in the study area. Further, the research supported a two-way causality between tourism and CO₂, where there is a unilateral causality from governance to CO₂. Similarly, a unidirectional causality was obtained from energy towards tourism. Lastly, the key policy recommendations based on the outcomes of the study are encouraging clean energy investment, enhancing good governance, and sustainable tourism development for improving environmental quality.

     

Environmental quality vs economic growth in a developing economy: complements or conflicts

An increase in economic activities which leads to economic growth has been adduced as a possible factor for environmental degradation. While some other studies have argued that as economies keep growing, there are possibilities for resource redistribution which could engender environmental balance, thus engendering the argument on the conflicting-complementary position of the environment-growth nexus. In the light of this, this study uses previous activities between economic activities and the environment to determine the conflicting or complementary relationship that exists between economic growth and the environment. Also, using Nigeria as a case study, the design of environmental growth nexus to achieving sustainable development is assessed. Annual time series data between 1970 and 2014 were sourced from the World Development Indicators. Following the neoclassical perspective on ecological growth and the Kuznets inverted U-hypothesis on the environment-growth relations, stationarity test was performed, and the autoregressive distributed lag estimates were employed. From the study, it is seen that factors like rainfall that promotes environmental quality in the long run promote economic growth (per capita and GDP growth) in Nigeria. Similarly, factors like natural resource utilization, which depletes environmental quality, increases economic growth but reduces economic growth per capita; thus, with questions for development, the possibility of a complementary relationship for environmental quality and economic growth is spotted if the right policies are ensured. Also, the study found evidence of a growing conflicting relation between environmental quality (CO₂) and economic growth (per capita and GDP growth). Meanwhile, these conflicts to a great extent find expression in the Kuznets hypothesis; such that, if policies that promote income per capita reduces pollution and pursues eco-efficiency via economic growth are properly harnessed, there are the prospects of meeting up with the goals of environmental sustainability in developing economies.

     

The impact of CO2 capture in the power and heat sector on the emission of SO2, NOx,particulate matter,volatile organic compounds and NH3 in the European Union

This study quantifies the trade-offs and synergies between climate and air quality policy objectives for the European power and heat (P&H) sector. An overview is presented of the expected performance data of CO₂ capture systems implemented at P&H plants, and the expected emission of key air pollutants, being: SO₂, NO_X, NH₃, volatile organic compounds (VOCs) and particulate matter (PM). The CO₂ capture systems investigated include: post-combustion, oxyfuel combustion and pre-combustion capture.For all capture systems it was found that SO₂, NO_x and PM emissions are expected to be reduced or remain equal per unit of primary energy input compared to power plants without CO₂ capture. Increase in primary energy input as a result of the energy penalty for CO₂ capture may for some technologies and substances result in a net increase of emissions per kWh output. The emission of ammonia may increase by a factor of up to 45 per unit of primary energy input for post-combustion technologies. No data are available about the emission of VOCs from CO₂ capture technologies.A simple model was developed and applied to analyse the impact of CO₂ capture in the European P&H sector on the emission level of key air pollutants in 2030. Four scenarios were developed: one without CO₂ capture and three with one dominantly implemented CO₂ capture system, varying between: post-combustion, oxyfuel combustion and pre-combustion.The results showed a reduction in GHG emissions for the scenarios with CO₂ capture compared to the baseline scenario between 12% and 20% in the EU 27 region in 2030. NO_x emissions were 15% higher in the P&H sector in a scenario with predominantly post-combustion and lower when oxyfuel combustion (?16%) or pre-combustion (?20%) were implemented on a large scale. Large scale implementation of the post-combustion technology in 2030 may also result in significantly higher, i.e. increase by a factor of 28, NH₃ emissions compared to scenarios with other CO₂ capture options or without capture. SO₂ emissions were very low for all scenarios that include large scale implementation of CO₂ capture in 2030, i.e. a reduction varying between 27% and 41%. Particulate Matter emissions were found to be lower in the scenarios with CO₂ capture. The scenario with implementation of the oxyfuel technology showed the lowest PM emissions followed by the scenario with a significant share allocated to pre-combustion, respectively ?59% and ?31%. The scenario with post-combustion capture resulted in PM emissions varying between 35% reduction and 26% increase.     

Environmental chamber test methodology for characterizing organic vapors from solid emission sources

Environmental test chambers are an important tool in the characterization of organic emissions from solid consumer and construction products and in the evaluation of their potential impact on indoor air quality. The results of extensive research concerning formaldehyde (CH₂O) emissions from such products strongly support this application of environmental chambers to measure product emissions and provide useful input for the design of environmental chamber studies. The physical design and test methodology for environmental chambers are strongly influenced by several elements in a comprehensive project plan for source characterization, including the selection process for test samples and the mathematical models used to interpret the organic emissions data. The protocol for environmental chamber testing extends broadly from the acquisition, preparation and conditioning of test specimens, to the selection and control of environmental test conditions, and to the calibration and measurement of system parameters and organic emissions. The requirements for environmental control inside the test chamber can be estimated from the sensitivity of the organic emission rates of the test products (e.g. CH₂O emissions from pressed-wood products) to variation in environmental parameters. The cost of the numerous, multiple-organic analyses required for environmental chamber testing of solid emitters is seen as a strong limitation to product selection strategies and modeling efforts. The modeling of organic emissions from solid emitters can be both a planning tool for development of chamber test methodology and a means to interpret test chamber results.     

Protection from wintertime rainfall reduces nutrient losses and greenhouse gas emissions during the decomposition of poultry and horse manure-based amendments

Manure-based soil amendments (herein “amendments”) are important fertility sources, but differences among amendment types and management can significantly affect their nutrient value and environmental impacts. A 6-month in situ decomposition experiment was conducted to determine how protection from wintertime rainfall affected nutrient losses and greenhouse gas (GHG) emissions in poultry (broiler chicken and turkey) and horse amendments. Changes in total nutrient concentration were measured every 3 months, changes in ammonium (NH₄⁺) and nitrate (NO₃^?) concentrations every month, and GHG emissions of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) every 7–14 days. Poultry amendments maintained higher nutrient concentrations (except for K), higher emissions of CO₂ and N₂O, and lower CH₄ emissions than horse amendments. Exposing amendments to rainfall increased total N and NH₄⁺ losses in poultry amendments, P losses in turkey and horse amendments, and K losses and cumulative N₂O emissions for all amendments. However, it did not affect CO₂ or CH₄ emissions. Overall, rainfall exposure would decrease total N inputs by 37% (horse), 59% (broiler chicken), or 74% (turkey) for a given application rate (wet weight basis) after 6 months of decomposition, with similar losses for NH₄⁺ (69–96%), P (41–73%), and K (91–97%). This study confirms the benefits of facilities protected from rainfall to reduce nutrient losses and GHG emissions during amendment decomposition.

Implications: The impact of rainfall protection on nutrient losses and GHG emissions was monitored during the decomposition of broiler chicken, turkey, and horse manure-based soil amendments. Amendments exposed to rainfall had large ammonium and potassium losses, resulting in a 37–74% decrease in N inputs when compared with amendments protected from rainfall. Nitrous oxide emissions were also higher with rainfall exposure, although it had no effect on carbon dioxide and methane emissions. Overall, this work highlights the benefits of rainfall protection during amendment decomposition to reduce nutrient losses and GHG emissions.     

Greenhouse gas emissions from dairy open lot and manure stockpile in northern China: A case study

The open lots and manure stockpiles of dairy farm are major sources of greenhouse gas (GHG) emissions in typical dairy cow housing and manure management system in China. GHG (CO₂, CH₄ and N₂O) emissions from the ground level of brick-paved open lots and uncovered manure stockpiles were estimated according to the field measurements of a typical dairy farm in Beijing by closed chambers in four consecutive seasons. Location variation and manure removal strategy impacts were assessed on GHG emissions from the open lots. Estimated CO₂, CH₄ and N₂O emissions from the ground level of the open lots were 137.5±64.7 kg hd^-1 yr^-1, 0.45±0.21 kg hd^-1 yr^-1 and 0.13±0.08 kg hd^-1 yr^-1, respectively. There were remarkable location variations of GHG emissions from different zones (cubicle zone vs. aisle zone) of the open lot. However, the emissions from the whole open lot were less affected by the locations. After manure removal, lower CH₄ but higher N₂O emitted from the open lot. Estimated CO₂, CH₄ and N₂O emissions from stockpile with a stacking height of 55±12 cm were 858.9±375.8 kg hd^-1 yr^-1, 8.5±5.4 kg hd^-1 yr^-1 and 2.3±1.1 kg hd^-1 yr^-1, respectively. In situ storage duration, which estimated by manure volatile solid contents (VS), would affect GHG emissions from stockpiles. Much higher N₂O was emitted from stockpiles in summer due to longer manure storage.

Implications: This study deals with greenhouse gas (GHG) emissions from open lots and stockpiles. It’s an increasing area of concern in some livestock producing countries. The Intergovernmental Panel on Climate Change (IPCC) methodology is commonly used for estimation of national GHG emission inventories. There is a shortage of on-farm information to evaluate the accuracy of these equations and default emission factors. This work provides valuable information for improving accounting practices within China or for similar manure management practice in other countries.     

Engineered nanomaterials in rivers--exposure scenarios for Switzerland at high spatial and temporal resolution

Probabilistic material flow analysis and graph theory were combined to calculate predicted environmental concentrations (PECs) of engineered nanomaterials (ENMs) in Swiss rivers: 543 river sections were used to assess the geographical variability of nano-TiO₂, nano-ZnO and nano-Ag, and flow measurements over a 20-year period at 21 locations served to evaluate temporal variation. A conservative scenario assuming no ENM removal and an optimistic scenario covering complete ENM transformation/deposition were considered. ENM concentrations varied by a factor 5 due to uncertain ENM emissions (15%-85% quantiles of ENM emissions) and up to a factor of 10 due to temporal river flow variations (15%-85% quantiles of flow). The results indicate highly variable local PECs and a location- and time-dependent risk evaluation. Nano-TiO₂ median PECs ranged from 11 to 1′623 ng L⁻¹ (conservative scenario) and from 2 to 1′618 ng L⁻¹ (optimistic scenario). The equivalent values for nano-ZnO and nano-Ag were by factors of 14 and 240 smaller.