Local governance of greenhouse gas emissions from air travel

Global greenhouse gas emissions from air travel (GHG-A) are on the rise, and projections point towards a rapid growth in the coming decades. This study aims to examine how local government (cities), addresses GHG-A in their Sustainable Energy Action Plans (SEAP). To fulfil this aim, over 200 SEAPs were analysed focusing on three issues: (1) Treatment of GHG-A in local emissions inventories; (2) Policy initiatives within this domain; and (3) The cities’ perceptions of the conflicts of interests. Results showed that more than half of the cities acknowledge the challenge of GHG-A, around one third include GHG-A in their emissions inventories, and more than one quarter have initiated policy interventions. To categorise these interventions, we have added a mode ‘governing by agenda setting’ to an existing analytical framework, ‘Modes of governing’. With their authority limited to the local setting, this mode of governing is a common channel for cities to push changes at higher levels.     

Climate pioneership and leadership in structurally disadvantaged maritime port cities

ABSTRACT

Innovative climate governance in small-to-medium-sized structurally disadvantaged cities (SDCs) are assessed. Considering their deeply ingrained severe economic and social problems it would be reasonable to assume that SDCs act primarily as climate laggards or at best as followers. However, novel empirical findings show that SDCs are capable of acting as climate pioneers. Different types and styles of climate leadership and pioneership and how they operate within multi-level and polycentric governance structures are identified and assessed. SDCs seem relatively readily willing to adopt transformational climate pioneership styles to create ‘green’ jobs, for example, in the offshore wind energy sector and with the aim of improving their poor external image. However, in order to sustain transformational climate pioneership they often have to rely on support from ‘higher’ levels of governance. For SDCs, there is a tension between learning from each other’s best practice and fierce economic competition in climate innovation.     

The adoption of a Climate Disaster Resilience Index in Chennai,India

Results derived from the Climate Disaster Resilience Index (CDRI)—consisting of five dimensions (economic, institutional, natural, physical, and social), 25 parameters, and 125 variables—reflect the abilities of people and institutions to respond to potential climate‐related disasters in Chennai, India. The findings of this assessment, applied in the 10 administrative zones of the city, reveal that communities living in the northern and older parts of Chennai have lower overall resilience as compared to the flourishing areas (vis‐à‐vis economic growth and population) along the urban fringes. The higher resilience of communities along the urban fringes suggests that urbanisation may not necessarily lead to a deterioration of basic urban services, such as electricity, housing, and water. This indication is confirmed by a strong statistical correlation between physical resilience and population growth in Chennai. The identification of the resilience of different urban areas of Chennai has the potential to support future planning decisions on the city's scheduled expansion.     

Abundance of harbour porpoises (Phocoena phocoena) in the western Baltic,Belt Seas and Kattegat

In July 2012, a ship-board double-platform line-transect survey was conducted to assess harbour porpoise (Phocoena phocoena) abundance in the Kattegat, Belt Seas and the Western Baltic. A total of 826 km of track lines were surveyed between the 2nd and 21st of July 2012, and 169 observations were made by the primary observers, comprising a total of 230 porpoises. Fifty-seven observations were identified as duplicate sightings observed by both tracker and primary observers and were used to correct for availability and perception bias of the primary detections. Using Mark–Recapture Distance Sampling analysis, we produced a model using the half-normal key function, including sightability as the only covariate to estimate the density and abundance of harbour porpoise within the 51,511 km² survey area. Estimated detection probability on the transect line, known as g(0), was at 0.571 (±0.074; CV = 0.130). Using a point independence model of the detection function, the abundance of harbour porpoises within the survey area was estimated at 40,475 animals (95 % CI 25,614–65,041, CV = 0.235) with an associated density of 0.786 animals km^?2 (95 % CI 0.498–1.242, CV = 0.235) and an average group size of 1.488 animals. These results reflect densities obtained during the SCANS surveys in 1994 and 2005, indicating no significant population trend in the area. However, it should be noted that the survey area covers more than one population and that results are therefore not necessarily reflecting local population trends. Until proper population borders are obtained, the abundance estimate provides baseline data for future monitoring and is an important input to the assessment of the conservation status of harbour porpoises in the area.     

Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland

We describe a probabilistic sampling design of circular permanent plots for the long-term monitoring of protected dry grasslands in Switzerland. The population under study is defined by the perimeter of a national inventory. The monitoring focus is on the species composition of the protected grassland vegetation and derived conservation values. Efficient trend estimations are required for the whole country and for some predefined target groups (six biogeographical regions and eleven vegetation types). The target groups are equally important regardless of their size. Consequently, intensified sampling of the less frequent groups is essential for sample efficiency. The prior information needed to draw a targeted sample is obtained from the sampling frame and external databases. The logistics and generalized delineation of the target population may pose further problems. Thus, investments in fieldwork and travel time should be well balanced by selecting a cluster sample. Second, any access problems in the field and non-target units in the sample should be compensated for by selecting reserve plots as they otherwise may considerably reduce the effective sample size. Finally, the design has to be flexible as the sampling frame may change over time and sampling intensity might have to be adjusted to redefined budgets or requirements. Likewise, the variables and biological items of interest may change. To fulfil all these constraints and to optimally use the available prior information, we propose a multi-stage self-weighted unequal probability sampling design. The design uses modern techniques such as: balanced sampling, spreading, stratified balancing, calibration, unequal probability sampling and power allocation. This sampling design meets the numerous requirements of this study and provides a very efficient estimator.