| 1. |
- Gren, Ing-Marie, et al.
(author)
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Costs of air pollutants from shipping : a meta-regression analysis
- 2020
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In: Transport reviews. - : Taylor & Francis. - 0144-1647 .- 1464-5327. ; 40:4, s. 411-428
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Journal article (peer-reviewed)abstract
- This study estimated the external cost of air pollution from shipping by means of a meta-regression analysis, which has not been made before. Three pollutants, which were included in most of the primary studies, were considered: nitrogen oxides (NOx), sulphur dioxides (SO2) and particulate matters with a diameter of max 2.5 micrometres (PM2.5). All primary studies included damages of health and a majority added impacts on agriculture and estimated the cost of air pollutants by transferring cost estimates from studies on costs of air emissions from transports in Europe. Different regression models and estimators were used and robust results were found of statistically significant emission elasticities of below one, i.e. total external costs increase by less than 1% when emissions increase by 1%. There was a small variation between the pollutants, with the highest elasticity for PM2.5 and lowest for NOx. Calculations of the marginal external cost of the pollutants showed the same pattern, with this cost being approximately six times higher for PM2.5 than for the other pollutants. Common to all pollutants was that the marginal external cost decreases when emission increases. Another robust result was a significant increase in the cost of studies published in journals compared with other publication outlets. These findings point out some caution when transferring constant external unit cost of air pollutant from shipping, which is much applied in the literature, and the cost functions estimated in this study could thus provide a complementary transfer mechanism.
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| 2. |
- Gren, Ing-Marie, et al.
(author)
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Effects of shipping on non-indigenous species in the Baltic Sea
- 2022
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In: Science of the Total Environment. - Amsterdam : Elsevier. - 0048-9697 .- 1879-1026. ; 821
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Journal article (peer-reviewed)abstract
- Shipping is regarded as an important vector for aquatic non-indigenous species (ANIS) worldwide. Less attention has been paid to its role in relation to environmental and economic causes of introduction and establishment, the knowledge of which is necessary to assess effects of changes in regulations on shipping. The purpose of this study was to estimate the impact of shipping on the incidence of ANIS in the Baltic Sea compared with environmental and economic factors. To this end, a production function was estimated with count data on ANIS (response variable) and shipping, environmental and economic factors as explanatory variables. Regression results from different regression models showed that shipping has a significant impact on ANIS incidence and can account for up to 38% of the number of ANIS in the sea. Predictions of the impact of measures implementing the Convention for the Control and Management of Ships' Ballast Water and Sediment indicated a reduction by 17% in the number of ANIS, which was counteracted by an expected increase in shipping traffic. © 2022
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| 3. |
- Gren, Ing-Marie, et al.
(author)
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Using traffic data to estimate wildlife populations
- 2015
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In: Journal of Bioeconomics. - : Springer. - 1387-6996 .- 1573-6989. ; 18:1, s. 17-31
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Journal article (peer-reviewed)abstract
- Wildlife populations are threatened worldwide by, among others, habitat fragmentation and hunting pressure. An important impediment for the large scale, national and regional, management of the populations is the difficulty to quantify population dynamics. The purpose of this study is to present a tool for such estimations which is based on available data in several countries; traffic load and traffic accidents with wildlife. An econometric model is developed, which accounts for landscape characteristics. It is applied to wild boar in Sweden, for which data on traffic load and accidents for different counties and years are available. Landscape characteristics are introduced with direct or indirect effects on population growth. The indirect landscape model gives the best statistical performance, and the results show relatively small differences in calculated intrinsic growth rate among counties but considerable differences in predicted population developments.
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