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Abstract: Will future transportation carbon dioxide (CO2) emissions per capita in Asia Pacific economies follow historical trends of the now developed world? Evidence to date is inconclusive. A comparison at similar income levels (purchasing power parity) between recent emissions in Asia Pacific countries and historical emissions in developed countries suggests diverging patterns. (A) High‐income Asia economies (Japan, Hong Kong and Singapore –‘low emitters’) exhibit lower emissions than a selected sample of seven developed countries (United States, Australia, France, the Netherlands, Italy, Sweden and United Kingdom). (B) Another set of Asian countries (South Korea and Taiwan –‘medium emitters’) follow the emissions trends of European countries, which are lower than those of Australia and the United States. (C) A third Asian group (Malaysia and Thailand –‘high emitters’) exhibit emission trends comparable to that of Australia. We describe these trends, examine their causes and extrapolate likely futures for emissions in low‐income Asia Pacific economies (China, Indonesia, Philippines and Vietnam). Although such predictions are speculative, the available evidence suggests that road CO2 emissions for Indonesia, the Philippines and Vietnam will follow those of the third group (high emitters), while those for China may follow either Group B or Group C. 相似文献
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Root respiration often exhibits a direct and immediate decline with increasing concentrations of ambient soil carbon dioxide concentration ([CO(2)]), and recent evidence suggests this decline may be attributable to a decline in maintenance respiration within the root. If true, this concept could provide a clue to the biochemical process underlying respiratory inhibition as well as improve our knowledge of the timing and degree to which this inhibition occurs in nature. To test the hypothesis that maintenance respiration exhibits a direct, negative response to increasing [CO(2)], we measured total respiration in intact root systems of western hemlock (Tsuga heterophylla (Raf.) Sarg.) seedlings grown at different relative growth rates and exposed to soil [CO(2)]s ranging from 91 to 7008 &mgr;mol mol(-1). Analysis of covariance was used to separate maintenance from total respiration. Total respiration declined exponentially with increasing [CO(2)]. Maintenance respiration, which comprised 85% of total respiration over all treatments, also declined exponentially with increasing [CO(2)]. Growth respiration was not inhibited at any [CO(2)]. These findings may explain why roots of some fast-growing species do not show [CO(2)] inhibition. 相似文献
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Model validation is often realized as a test of how well model predictions match a set of independent observations. One would think that the burden of proof should rest with the model, to force it to show that it can make accurate predictions. Further, one would think that increasing the sample size ought to increase the model's ability to demonstrate its utility. Traditional statistical tools are inappropriate for this because they default to the case that the model and the data are no different, and their ability to detect differences increases with the sample size. These traditional tools are optimized to detect differences, rather than similarities. We present an alternative strategy for model validation that is based on regression and statistical tests of equivalence. Equivalence tests reverse the usual null hypothesis: they posit that the populations being compared are different and use the data to prove otherwise. In this sense, equivalence tests are lumping tests, whereas the traditional statistical tests are splitting tests. To date, model validation with equivalence tests has focused on comparisons of means. Our proposed test checks not only for similarity of means, but also for similarity between individual predictions and observations. The strategy is demonstrated using three case studies that differ in their modeling objectives, and for varied sample sizes. The proposed strategy provides a formal means of model validation that is superior to traditional statistical tests in each case. 相似文献
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Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests. 相似文献
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Soil surface CO(2) flux (F(s)) is the dominant respiratory flux in many temperate forest ecosystems. Snowpacks increase this dominance by insulating the soil against the low temperature to which aboveground components are exposed. However, measurement of F(s) in winter may be impeded by snow cover. Likewise, developing annual F(s) models is complicated by seasonal variation in root and microbial metabolism. We compared three methods of measuring sub-snow F(s): (1) dynamic chamber measurements at the upper snowpack surface (F(snow)), (2) dynamic chamber measurements at the soil surface via snowpits (F(soil)), and (3) static estimates based on measured concentrations of carbon dioxide ([CO(2)]) and conductance properties of the snowpack (F(diffusional)). Methods were compared at a mid-elevation forest in northeastern Washington, a mid-elevation forest in northern Idaho, and a high-elevation forest and neighboring meadow in Wyoming. The methods that minimized snowpack disturbance, F(diffusional) and F(snow), yielded similar estimates of F(s). In contrast, F(soil) yielded rates two to three times higher than F(snow) at the forested sites, and seven times higher at the subalpine meadow. The ratio F(soil)/F(snow) increased with increasing snow depth when compared across all sites. Snow removal appears to induce elevated soil flux as a result of lateral CO(2) diffusion into the pit. We chose F(snow) as our preferred method and used it to estimate annual CO(2) fluxes. The snowpack was present for 36% of the year at this site, during which time 132 g C m(-2), or 17% of the annual flux, occurred. We conclude that snowpack CO(2) flux is quantitatively important in annual carbon budgets for these forests and that the static and dynamic methods yield similar and reasonable estimates of the flux, as long as snowpack disturbance is minimized. 相似文献