Institute for Environmental Policy

Some features of the description of the evolution of processes of degradation and destruction of materials are considered, based on an analysis of changes in their energy state under the influence of various types of loading. The factors that determine the complexity of creating a generalized model of the destruction of materials of parts are noted, the main one of which is the search for the relationship between numerous interdependent parameters that characterize not only the loading process, but also the process of adaptation of materials to changing conditions of dynamic influences.

Currently, increasing the mass of carbon dioxide emissions is regarded as the main factor of increasing the greenhouse effect that is considered by the scientists to be the cause of the global warming. The problem has stimulated the development and implementation of various options of decarbonization including the sequestration technology (CCS – capture and storage) and carbon dioxide utilization (CC(U)S technology – carbon capture, utilization and storage). The technologies are aimed at reducing carbon dioxide emissions by capturing the gas at the industrial premises (rarely from the atmosphere) and can be regarded as complementary and crucially essential for achieving carbon neutrality as they allow avoiding radical transformations of energy and industrial processes. The study is directed towards analyzing the world experience and the essence of CC(U)S initiatives to develop complex classification of sequestration projects and use of CO2 to systematize the existing knowledge and practical aspects on the subject. The authors suggest a variant of complex classification of the projects that involves grouping the features by basic, technological and organizational and economic characteristics and identifying different types of projects according to the suggested features. In order to test the classification the authors introduce a classification profile on the existing CC(U)S projects implemented in the USA, the UK and China. The findings can be used for planning the activities in the area of development of sequestration and carbon dioxide utilization technologies in Russia, for making decisions on these initiatives including those regarding financing and support of such projects at the government level.

Abstract Large‐scale fishing effort in the waters of tropical and lower income countries is predominantly driven by ‘distant water fishing fleets’ often owned by companies based in a small number of countries and has been associated with a range of negative environmental and social outcomes. West Africa is an example where such fleets are a dominant feature. In the waters of Guinea‐Bissau, Guinea, Sierra Leone, Liberia and Ghana, 75% of all licensed bottom trawl vessels in 2017 either registered (‘flagged’) or largely owned in China. In this context, we examined the size and distribution of the economic outcomes from operation of these vessels in above five countries to better understand the impacts of these coastal states’ decision for allocating access to the distant water bottom trawl fleet. Based on analysis of heterogeneous sources of data, we estimate that in 2017 the China‐flagged or owned coastal bottom trawl fleets generated little to no net economic benefits in terms of resource rent for either the companies or the coastal states in Sierra Leone, Liberia and Ghana. Significantly higher economic benefits were generated from the offshore fishery in Guinea‐Bissau (US$31 million) and Guinea (US$25–38 million), largely captured by the companies owning the China‐flagged fleet. The size and distribution of the resource rent suggest that based solely of economic measures, the five coastal states may want to better assess the terms of these access arrangements for the foreign‐flagged or owned bottom trawl fleets. The fishing companies may need to reassess the sustainability of their operations, particularly in the southern states for long‐term economic benefits.

Improved damage models put social cost of carbon estimates on a firmer footing

Intet resumé

The U.S. economy has grown to be the world's largest, even in the face of the most varied and costly weather and climate extremes on the planet (see http://www.munichreamerica.com/webinars/2013_01_natcatreview/MunichRe_III_NatCat01032013.pdf). Nevertheless, these extremes continue to take a toll on the nation, diverting public and private funds while limiting economic growth and jobs and threatening the well‐being of Americans. Extreme weather events affect every state and manifest differently by region (see Figure 1 in Supporting Information in the online version of this Forum and http://www.ncdc.noaa.gov/billions/summary‐stats).

George Marcus (1995:96), in a 1995 paper, defined multi-sited ethnography as "moving out from single sites and local situations of conventional ethnographic research designs to circulation of cultural meanings, objects, and identities in diffuse time-space." In the 18 years since, multi-sited ethnography as an object of study and practice has gained immense popularity. Both scholars and practitioners have applied the concept to many phenomena, including migrations (Fitzgerald 2006) and commodity chains (Bestor 2001; Freidberg 2001). Several recent books explore the concept in depth (Coleman and Von Hellermann 2011; Falzon 2009). However, little of this work has directly focused upon organizational ethnographies, and less still has examined how applied anthropologists inside and outside of academia can design projects to ensure benefit to those informants who make these ethnographies possible.

Abstract Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project‐scale carbon accounting for changes in land use and management and are used in frameworks for Life Cycle Assessment and carbon footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can introduce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop greenhouse gas (GHG) accounting to facilitate decision making in the land‐based sectors. Higher tier methods have been defined by IPCC and must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop type, topography), and must account for land use transitions and management changes being implemented. Furthermore, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine spatial resolution, covering the meteorological, soil, cropping system and management domains, with quantified uncertainties. Testing the reliability of the models will require data either from sites with repeated measurements or from chronosequences. We review current global capability for estimating changes in soil carbon at fine spatial scales and present a vision for a framework capable of quantifying land use change and management impacts on soil carbon, which could be used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indirect impacts of land use change. The aim of this framework is to provide a globally accepted standard of carbon measurement and modelling appropriate for GHG accounting that could be applied at project to national scales (allowing outputs to be scaled up to a country level), to address the impacts of land use and land management change on soil carbon.

Nations around the world are considering strategies to mitigate the severe impacts of climate change predicted to occur in the twenty-first century. Many countries, however, lack the wealth, technology, and government institutions to effectively cope with climate change. This study investigates the varying degrees to which developing and developed nations will be exposed to changes in three key variables: temperature, precipitation, and runoff. We use Geographic Information Systems (GIS) analysis to compare current and future climate model predictions on a country level. We then compare our calculations of climate change exposure for each nation to several metrics of political and economic well-being. Our results indicate that the impacts of changes in precipitation and runoff are distributed relatively equally between developed and developing nations. In contrast, we confirm research suggesting that developing nations will be affected far more severely by changes in temperature than developed nations. Our results also suggest that this unequal impact will persist throughout the twenty-first century. Our analysis further indicates that the most significant temperature changes will occur in politically unstable countries, creating an additional motivation for developed countries to actively engage with developing nations on climate mitigation strategies.<p></p>

ABSTRACT Declining fish health and the occurrence of large fish kills are some of the more publicly meaningful indicators of water quality in the impaired Neuse River Estuary, North Carolina. It is generally believed that such problems are caused by the widespread depletion of dissolved oxygen—an indirect result of anthropogenic nutrient pollution. However, the development of scientific simulation models to predict how improvements in oxygen conditions will improve the health of fish and reduce the frequency of fish kills has proven elusive. As a pragmatic solution to this problem, the expert opinion of estuarine fisheries scientists in possession of relevant data and experience was elicited. The relations between joint and conditional probabilities were exploited to translate quantities that are normally hard to assess into quantities that can be drawn more directly from the experiential knowledge of the experts. A combined model of expert opinion was constructed as an influence diagram, and Monte Carlo simulation was used to generate predictions of fish health and fish kills in the Neuse River Estuary under current and improved oxygen conditions. Full model results are expressed as probability distributions, capturing the effects of natural variability and knowledge uncertainty—both contributors to total ecological risk.