Organic matter from salmon farms has been shown to be assimilated by soft sediment and rocky reef communities within the ecological footprint of salmon farms. Given these findings, another question arises – What other chemicals in salmon feed may be assimilated into wild communities via organic waste from salmon farms? Here we measured a suite of organic contaminants in salmon feed, in organisms used in a controlled feeding experiment, and in reef species collected within the depositional footprint of salmon farms. Gas Chromatography-Tandem Mass Spectrometry was used to quantify trace concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and current-use (CPUs) and historic-use pesticides (HUPs) in salmon feed imported to New Zealand. The effect of assimilation of farm-derived organic matter on contaminant profiles differed among species during the controlled feeding experiment and demonstrated that migration of individuals to a farm-associated site has the potential to increase or decrease organic contaminant concentrations. Concentrations of PCBs in Parapercis colias (blue cod), a highly resident, long-lived fish, were significantly higher at farm sites than at reference sites. While these concentrations were relatively low in a global context, this result presents blue cod as an important candidate for future monitoring of organic contaminants around point sources. PCBs and PBDEs measured in wild marine species were all below limits set by the European Union, whereas concentrations of certain HUPs, specifically dichlorodiphenyltrichloroethane (DDT) and its degradation products and endosulfan, may be of concern as a consequence of alternative anthropogenic activities. Overall, feed imported to New Zealand had relatively low levels of most organic contaminants that, at current levels, are unlikely to result in significant ecological effects to wild communities in adjacent habitats.

Submarine tailing disposal (STD) in fjords from land-based mines is common practice in Norway and takes place in other regions worldwide. We synthesize the results of a multidisciplinary programme on environmental impacts of STDs in Norwegian fjords, providing new knowledge that can be applied to assess and mitigate impact of tailing disposal globally, both for submarine and deep-sea activities. Detailed geological seafloor mapping provided data on natural sedimentation to monitor depositional processes on the seafloor. Modelling and analytical techniques were used to assess the behaviour of tailing particles and process-chemicals in the environment, providing novel tools for monitoring. Toxicity tests showed biological impacts on test species due to particulate and chemical exposure. Hypersedimentation mesocosm and field experiments showed a varying response on the benthos, allowing to determine the transition zone in the STD impact area. Recolonisation studies indicate that full community recovery and normalisation of metal leakage rates may take several decades due to bioturbation and slow burial of sulfidic tailings. The results are synthesised to provide guidelines for the development of best available techniques for STDs.

A major coal mine project in Queensland, Australia, is currently under review. It is planned to be located about 10 km away from the Great Barrier Reef World Heritage Area (GBRWHA). Sediment dispersal patterns and their impact on marine ecosystems have not been properly assessed yet. Here, we simulate the dispersal of different sediment types with a high-resolution ocean model, and derive their environmental footprint. We show that sediments finer than 32 μm could reach dense seagrass meadows and a dugong sanctuary within a few weeks. The intense tidal circulation leads to non-isotropic and long-distance sediment dispersal patterns along the coast. Our results suggest that the sediments released by this project will not be quickly mixed but rather be concentrated where the most valuable ecosystems are located. If accepted, this coal mine could therefore have a far-reaching impact on the GBRWHA and its iconic marine species.

The last decades have shown that human activities damaged environmental quality and biodiversity. As accepted the need of sustainable development, there should be balance between economic, social and environmental issues in the long term. Ecological footprint is an important indicator that shows how human activities reduce environmental quality among a specific region or country. In addition, human capital is mostly used indicator to estimate economic and environmental development level. In this study, it is aimed to explore the effect of human capital on fishing footprint for 10 Mediterranean Countries during the period 1995–2018. According to empirical evidences, it is concluded that for relatively low human capital level, human capital has negative effect on sustainability and in the relatively high level, it has positive effect on sustainability.

Bioplastics have emerged to minimize the ecological footprint of non-degradable plastics. However, the effect of their degradation in aquatic systems, including the interaction with toxic metals, is still unexplored. In this work, the influence of UV-aging on structure, chemistry, wettability, rigidity, and Hg-sorption of commercially available bioplastic (BIO)- and polyethylene (PE)-based films was studied. To mimetize the materials disposal in fresh-/saltwaters, non-saline/saline aqueous solutions were used in Hg-sorption studies. ATR-FTIR spectra revealed that the BIO film was a coblended starch/polyester-based material, whose microstructure, physicochemical, and mechanical properties changed after UV-aging to a higher extent than in PE film. AFM and kinetic modelling pointed out electrostatic interactions/complexation as the mechanisms involved in the increased Hg-sorption by the UV-aged BIO film. An increased salinity did not impair its Hg-sorption. Therefore, when disposed in aquatic systems, starch/polyester-based bioplastics can play a potential vector for amplifying Hg along the food chain.

Paying attention to human activities in terms of land grazing infrastructure, crops, forest products, and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. For the present study, global database data were used. The ability of the penalized regression (RR) approaches (including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 countries over the past two decades (1999–2018) was illustrated and compared. For this purpose, 10-fold cross-validation was used to evaluate the predictive performance and determine the penalty parameter for PR models. According to the results, the predictive performance compared to linear regression improved somewhat using the penalized methods. Using the elastic net model, more global macro indices were selected than Lasso. Although Lasso selected only a few indicators, it had better predictive performance among PR ns models. In addition to relative improvement in the predictive performance of PR methods, their interest in selecting a subset of indicators by shrinking coefficients and creating a parsimonious model was evident and significant. As a result, PR methods would be preferred, using variable selection and interpretive considerations to predictive performance alone. On the other hand, ANN models with higher determination coefficients (R²) and lower RMSE values performed significantly better than PR and OLS and showed that they were more accurate in predicting EF. Therefore, ANN could provide considerable and appropriate predictions for EF indicators in the G-20 countries.

There are many studies on the relationship between energy consumption and various environmental indicators in Africa, and SANE countries in particular. However, there is a dearth of studies that relate external debt to CO₂ emissions, and even the ecological footprint, which is a more comprehensive environmental indicator. As such, this paper applies advanced estimation techniques to explore the role of external debt in the famous energy-growth-environmental nexus in SANE countries from 1970 to 2018. The findings from the Augmented Mean Group estimator indicate that economic growth and energy consumption increase environmental pressure in the SANE countries. On country-level results, the environmental Kuznets curve (EKC) hypothesis, monotonic increase, and monotonic decrease for ecological footprint holds in South Africa, Algeria, and Nigeria, respectively. Also, the results reveal that external debt increases the ecological footprint in South Africa and Algeria. Furthermore, the Kónya (2006) bootstrap country-level Granger causality test shows that ecological footprint is sensitive to economic growth and energy consumption in South Africa and Nigeria, while economic growth is sensitive to the ecological footprint in both Algeria and Nigeria. This study argues that stringent policy suggestions should be centred on reducing the overdependence on non-renewable energy sources since it underscores the major deteriorating state of environmental quality across SANE countries.

The present study assesses the effect of public-private partnerships in energy and financial development on Brazil’s ecological footprint and also takes into account the role of renewable energy and economic growth using data spanning from 1983 to 2017. The study utilized several techniques including autoregressive distributive lag (ARDL) and dynamic ordinary least square (DOLS) to examine the relationship between ecological footprint and the determinants, while the gradual shift causality test was utilized to capture the causal linkage between the series in the presence of a single structural break. The outcomes of the Maki co-integration test revealed evidence of a long-run association among the variables of interest. Furthermore, the results of the ARDL and DOLS tests revealed that economic growth and public and private investment in energy increase environmental degradation, while it is mitigated by both renewable energy and financial development. Moreover, the gradual shift causality test revealed a bidirectional causal linkage between ecological footprint and economic growth. The present study recommends the establishment of a forum that will foster public and private partnerships to enhance communication, which will promote collaboration on new initiatives involving green technological innovations.

Ever since the emancipation of a country, its environmental quality has undergone a significant transition during the development phases; Bangladesh is no exception. Bangladesh is facing a serious threat in the age of global warming, and climate change as the country is looking forward in achieving the SDGs by 2030. Yet, there is a dearth of study regarding the relationship among crucial macroeconomic drivers and ecological footprint (a proxy for environmental degradation). Under the circumstances, this study explores the effects of economic growth, capital formation, urbanization, trade openness, energy use, and technological innovation on the ecological footprint by adopting the novel dynamic Autoregressive Distributed Lag (ARDL) simulations approach for Bangladesh, using annual frequency data from 1972 to 2017. Empirical results from the bounds test ascertained that there exists a long-run equilibrium association among the outlined variables. Furthermore, the novel dynamic ARDL simulation results revealed that Bangladesh is yet to achieve the environmental Kuznets curve (EKC) hypothesis. It was observed that the Bangladesh economy is still at the scale stage of its economic trajectory, emphasizing economic growth relative to her environmental status. However, capital formation, urbanization, and energy use seemed to degrade environmental quality, while trade openness and technological innovation upgraded the environmental quality. Putting it more elaborately, a unit escalation in GDP per capita increases the ecological footprint by 0.829% in the long run, while a unit increase in energy consumption upsurges the ecological footprint by 1.074% and 0.761% in the long run and short run, respectively. As regards technology innovation, one unit increase in it cutbacks the ecological footprint by 0.596% in the long run. Furthermore, the frequency domain causality unveiled the long-run feedback effect between economic growth and ecological footprint. The study further presents possible recommendations that can sustainably address environmental issues, keeping the economy buoyant.

For decades, environmental degradation has become a universal challenge, and for sustainable environment quality, a true and broader proxy is vital. Pakistan is an ecological deficient country in the world, being the sixth-largest economy (population-wise). This study investigates the prime sources of environmental degradation through ecological footprint in Pakistan. The yearly time-series data spanning 1972 to 2020 is utilized for a set of regressors as fossil fuel energy consumption, trade openness, arable land, industrial share to GDP, economic growth, and population growth. We use various econometric techniques, the bounds test, ARDL (short and long run) model, FMOLS, and Granger causality test. Bounds test confirms the existence of cointegration among variables included in our model. The ARDL estimates suggest that fossil fuel energy consumption, trade openness, and population growth are the leading factors affecting the environment. Fossil fuel consumption and population growth significantly damage the environment in the short and long run. Contrasting to that, trade openness is substantial to the environment quality. The FMOLS approves the robustness of the cointegrating findings. Moreover, a unidirectional causal relationship from economic growth to the ecological footprint (GDP → EFP). And also, the ecological footprint of arable land (EFP → AL) is witnessed. At the same time, bidirectional causality is found between growth rate and fossil energy consumption (GDP ↔ FEC). Lastly, we recommend some policy options to improve environmental quality in Pakistan.