Die höhere Vegetation der subalpinen Seen und Moorgebiete des Val Piora

Ohne Zusammenfassung     

Options to overcome the barriers to pricing European agricultural emissions

Although agriculture could contribute substantially to European emission reductions, its mitigation potential lies untapped and dormant. Market-based instruments could be pivotal in incentivizing cost-effective abatement. However, sector specificities in transaction costs, leakage risks and distributional impacts impede its implementation. The significance of such barriers critically hinges on the dimensions of policy design. This article synthesizes the work on emissions pricing in agriculture together with the literature on the design of market-based instruments. To structure the discussion, an options space is suggested to map policy options, focusing on three key dimensions of policy design. More specifically, it examines the role of policy coverage, instruments and transfers to farmers in overcoming the barriers. First, the results show that a significant proportion of agricultural emissions and mitigation potential could be covered by a policy targeting large farms and few emission sources, thereby reducing transaction costs. Second, whether an instrument is voluntary or mandatory influences distributional outcomes and leakage. Voluntary instruments can mitigate distributional concerns and leakage risks but can lead to subsidy lock-in and carbon price distortion. Third, the impact on transfers resulting from the interaction of the Common Agricultural Policy (CAP) with emissions pricing will play a key role in shaping political feasibility and has so far been underappreciated.

POLICY RELEVANCE

Following the 2015 Paris Agreement, European climate policy is at a crossroads. Achieving cost-effectively the 2030 and 2050 European targets requires all sectors to reduce their emissions. Yet, the cornerstone of European climate policy, the European Union Emissions Trading System (EU ETS), covers only about half of European emissions. Major sectors have been so far largely exempted from carbon pricing, in particular transport and agriculture. While transport has been increasingly under the spotlight as a possible candidate for an EU ETS sectoral expansion, policy discussions on pricing agricultural emissions have been virtually absent. This article attempts to fill this gap by investigating options for market-based instruments to reduce agricultural emissions while taking barriers to implementation into account.     

Escaping the climate policy uncertainty trap: options contracts for REDD+

Climate policy uncertainty significantly hinders investments in low-carbon technologies, and the global community is behind schedule to curb carbon emissions. Strong actions will be necessary to limit the increase in global temperatures, and continued delays create risks of escalating climate change damages and future policy costs. These risks are system-wide, long-term and large-scale and thus hard to diversify across firms. Because of its unique scale, cost structure and near-term availability, Reducing Emissions from Deforestation and forest Degradation in developing countries (REDD+) has significant potential to help manage climate policy risks and facilitate the transition to lower greenhouse gas emissions. ‘Call’ options contracts in the form of the right but not the obligation to buy high-quality emissions reduction credits from jurisdictional REDD+ programmes at a predetermined price per ton of CO₂ could help unlock this potential despite the current lack of carbon markets that accept REDD+ for compliance. This approach could provide a globally important cost-containment mechanism and insurance for firms against higher future carbon prices, while channelling finance to avoid deforestation until policy uncertainties decline and carbon markets scale up.

Key policy insights

• Climate policy uncertainty discourages abatement investments, exposing firms to an escalating systemic risk of future rapid increases in emission control expenditures.

• This situation poses a risk of an abatement ‘short squeeze,’ paralleling the case in financial markets when prices jump sharply as investors rush to square accounts on an investment they have sold ‘short’, one they have bet against and promised to repay later in anticipation of falling prices.

• There is likely to be a willingness to pay for mechanisms that hedge the risks of abruptly rising carbon prices, in particular for ‘call’ options, the right but not the obligation to buy high-quality emissions reduction credits at a predetermined price, due to the significantly lower upfront capital expenditure compared to other hedging alternatives.

• Establishing rules as soon as possible for compliance market acceptance of high-quality emissions reductions credits from REDD+ would facilitate REDD+ transactions, including via options-based contracts, which could help fill the gap of uncertain climate policies in the short and medium term.

     

Climate change adaptation of coffee production in space and time

Savannas constitute the most fire-prone vegetation type on earth and are a significant source of greenhouse gas emissions. Most savanna fires are lit by people for a variety of livelihood applications. ‘Savanna burning’ is an accountable activity under the Kyoto Protocol, but only Australia, as a developed economy, accounts for emissions from this source in its national accounts. Over the past decade considerable effort has been given to developing savanna burning projects in northern Australia, combining customary indigenous (Aboriginal) approaches to landscape-scale fire management with development of scientifically robust emissions accounting methodologies. Formal acceptance by the Australian Government of that methodology, and its inclusion in Australia’s developing emissions trading scheme, paves the way for Aboriginal people to commercially benefit from savanna burning projects. The paper first describes this Australian experience, and then explores options for implementing community-based savanna burning emissions reduction projects in other continental savanna settings, specifically in Namibia and Venezuela. These latter examples illustrate that savanna fire management approaches potentially have broader application for contributing to livelihood opportunities in other fire-prone savanna regions.     

Photochemical activation of chlorine by iron-oxide aerosol

The photochemical activation of chlorine by dissolved iron in artificial sea-salt aerosol droplets and by highly dispersed iron oxide (Fe₂O₃) aerosol particles (mainly hematite, specific surface ~150 m² g^?1) exposed to gaseous HCl, was investigated in humidified air in a Teflon simulation chamber. Employing the radical-clock technique, we quantified the production of gaseous atomic chlorine (Cl) from the irradiated aerosol. When the salt aerosol contained Fe₂O₃ at pH 6, no significant Cl production was observed, even if the dissolution of iron was forced by “weathering” (repeatedly freezing and thawing for five times). Adjusting the pH in the stock suspension to 2.6, 2.2, and 1.9 and equilibrating for one week resulted in a quantifiable amount of dissolved iron (0.03, 0.2, and 0.6 mmol L^?1, respectively) and in gaseous Cl production rates of ~1.6, 6, and 8?×?10²¹ atoms cm^?2 h^?1, respectively. In a further series of experiments, the pure Fe₂O₃ aerosol was exposed to various levels of gaseous hydrogen chloride (HCl). The resulting Cl production rates ranged from 8?×?10²⁰ Cl atoms cm^?2 h^?1 (at ~4 ppb HCl) to 5?×?10²² Cl atoms cm^?2 h^?1 (at ~350 ppb HCl) and confirmed the uptake and conversion of HCl to atomic Cl (at HCl to Cl conversion yields of 2–5 %, depending on the relative humidity). The Fe₂O₃ experiments indicate that iron-induced Cl formation may be important for highly soluble combustion-aerosol particles in marine environments in the presence of gaseous HCl.     

Remote sensing and GIS based determination of groundwater dependent ecosystems in the Western Cape, South Africa

Finding the location of groundwater dependent ecosystems (GDEs) is important in determining the extent of restrictions that need to be placed upon the abstraction of groundwater. Remote sensing was combined with geographical information system (GIS) modelling to produce a GDE probability rating map for the Sandveld region, South Africa. Landsat TM imagery identified the areas indicating the probable presence of GDEs and GIS assisted in their delineation. Three GIS models were generated: a GIS model predicting landscape wetness potential (LWP model) based on terrain morphological features; the LWP model was modified to highlight groundwater generated landscape wetness potential (the resulting GglWP model); and a groundwater elevation model was interpolated, combining groundwater level measurements in boreholes in the region with digital elevation model data. Biomass indicators generated from Landsat were classified and combined with the GIS models, followed by field verification of riverine and wetland GDEs. The LWP model provided the most accurate results of the three models tested for GDEs in this region.

A New Method of Significance Testing for Correlation-Coefficient Fields and Its Application

Correlation-coefficient fields are widely used in short-term climate prediction research. The most frequently used significance test method for the correlation-coefficient field was proposed by Livezey, in which the number of significantcorrelation lattice(station) points on the correlation coherence map is used as the statistic. However, the method is based on two assumptions:(1) the spatial distribution of the lattice(station) points is uniform;and(2) there is no correlation between the physical quantities in the correlation-coefficient field. However, in reality, the above two assumptions are not valid.Therefore, we designed a more reasonable method for significance testing of the correlation-coefficient field. Specifically, a new statistic, the significant-correlation area, is introduced to eliminate the inhomogeneity of the grid(station)-point distribution, and an empirical Monte Carlo method is employed to eliminate the spatial correlation of the matrix.Subsequently, the new significance test was used for simultaneous correlation-coefficient fields between intensities of the atmospheric activity center in the Northern Hemisphere and temperature/precipitation in China. The results show that the new method is more reasonable than the Livezey method.     

Determination of Mislocation Vectors to Evaluate bias at GSETT-3 primary stations

Using the database provided by the Reviewed Event Bulletins (REBs) for the first 2.5 years of the Group of Scientific Experts Technical Test-3 (GSETT-3) experiment, we compiled mislocation vectors for both arrays and selected three-component stations of the primary network from the published slowness and azimuth information gained through f-k- and polarization analysis. Imposing constraints such as a minimum signal-to-noise ratio (SNR) and number of defining phases, we aim at eliminating location bias as the hypocentral parameters are taken from the REBs. Results from 14 arrays with apertures from about 1 km to more than 20 km are presented as well as from 18 three-component stations, which indicate that the mislocation vectors in many cases can improve location accuracy considerably. If these mislocation vectors are compiled to provide coverage of a sufficient portion of the slowness domain these empirical corrections can easily be applied prior to location processing. In the context of the Comprehensive Nuclear Test-Ban Treaty (CTBT), these mislocation patterns could be essential for providing accurate event location of suspicious low-magnitude events, as these location parameters will be used to pinpoint the area where to conduct an on-site inspection.     

Expectation Maximization algorithm and its minimal detectable outliers

Minimal Detectable Biases (MDBs) or Minimal Detectable Outliers for the Expectation Maximization (EM) algorithm based on the variance-inflation and the mean-shift model are determined for an example. A Monte Carlo method is applied with no outlier and with one, two and three randomly chosen outliers. The outliers introduced are recovered and the corresponding MDBs are almost independent from the number of outliers. The results are compared to the MDB derived earlier by the author. This MDB approximately agrees with the MDB for one outlier of the EM algorithm. The MDBs for two and three outliers are considerably larger than MDBs of the EM algorithm.     

Approximating covariance matrices estimated in multivariate models by estimated auto- and cross-covariances

Quantities like tropospheric zenith delays or station coordinates are repeatedly measured at permanent VLBI or GPS stations so that time series for the quantities at each station are obtained. The covariances of these quantities can be estimated in a multivariate linear model. The covariances are needed for computing uncertainties of results derived from these quantities. The covariance matrix for many permanent stations becomes large, the need for simplifying it may therefore arise under the condition that the uncertainties of derived results still agree. This is accomplished by assuming that the different time series of a quantity like the station height for each permanent station can be combined to obtain one time series. The covariance matrix then follows from the estimates of the auto- and cross-covariance functions of the combined time series. A further approximation is found, if compactly supported covariance functions are fitted to an estimated autocovariance function in order to obtain a covariance matrix which is representative of different kinds of measurements. The simplification of a covariance matrix estimated in a multivariate model is investigated here for the coordinates of points of a grid measured repeatedly by a laserscanner. The approximations are checked by determining the uncertainty of the sum of distances to the points of the grid. To obtain a realistic value for this uncertainty, the covariances of the measured coordinates have to be considered. Three different setups of measurements are analyzed and a covariance matrix is found which is representative for all three setups. Covariance matrices for the measurements of laserscanners can therefore be determined in advance without estimating them for each application.