ANALYSIS OF THE EMISSION TRADING POTENTIAL IN SRI LANKA FOR GLOBAL GREENHOUSE GAS MARKET UNDER THE KYOTO PROTOCOL

Under the United Nations Framework Convention of Climate Change (UNFCCC) reductionof Green House Gas (GHG) emissions become a global good with shared and differentiatedresponsibility vested with member countries. The Kyoto Protocol was adopted in 191)7 asthe legally hinding instrument to achieve the objectives of UNFCCC. This protocolintroduced three controversial mechanisms namely Joint Implementation (11. Article 6).Clean Development Mechanism (CDM, Article 12) and the emission trading (Article J 7) furthe establishment of markets for GHG emission reduction.Under the Annex I of UNFCCC countries are obliged to reduce their GHG by 5.2'7< fromthe total 1990 level. Global commitments under the common but di Ilcrentiatcdresponsihility principle of UNFCCC for reducing the emissions vary and depends on thecountry's level of emission. Accordingly Annex I countries were given emission reductiontargets c.g. Japan 6Lk. EU 8L.k. and US 7CJL. This issue has drawn attention or the developedcountries since it could alter their lifestyles drastically. The flexible mechanism permitsdeveloped countries to purchase GHG emission potential from developing countriesSelling GHG emission potential (although an income source) has been viewed as sellingdevelopment potential of developing countries. This puts the developing countries in adilemma in making decisions on emission trading. Therefore an in-depth knowledge onmarket potential of GHG is important.The objective of this paper is to review the flexible mechanisms under the Kyoto Protocoli.e. 11, CDM and emission trading along with principles. modalities and procedures inrelation to Sri Lankan environmental conditions and to estimate the total GHG marketpotential for Sri Lanka if the country decides to participate in the global GHG market. Thispaper presents an economic analysis of GHG market in Sri Lanka with an attempt toinvestigate the relationship between rate of emission and economic growth. This ventureessentially creates an equity problem which is discussed using different discount rates.Data from secondary sources. in particular GHG inventories for Sri Lanka for J 1)94 & 11)1)5years arc used to estimate Sri Lankan emission trading potential. These figures will heuseful for predicting Sri Lankan contribution to the emission trading market. Sinks andSources and the sectors of emission are discussed separately in order to identify the mostimportant sectors in terms of emission trading. The paper also discusses the disadvantagesof emission trading, particularly whether this would limit our development potential andsovereignty. the major criticisms against the emission trading. Finally, this paper presentsthe relationship between GHG emission. emission trading potential and economicdevelopment under various scenarios.

Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis

Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales

Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis

International audienceAbstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

International audienc

A model-data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program Site Synthesis

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO{sub 2} exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO{sub 2} exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans {approx}220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO{sub 2} exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was {approx}10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step