Environmental Kuznets Curves in the People’s Republic of China: turning points and regional differences

This paper examines the relationship between economic growth and environmental sustainability in the People’s Republic of China by empirically estimating environmental Kuznets curve (EKC) models using provincial-level panel data from 1985 to 2005. The results show that there exists an inverted-U shaped relationship as hypothesized by the EKC model between per capita income and per capita emissions (or discharges) in the cases of waste gas from fuel burning and waste water, with a turning point at per capita gross domestic product of $12,903 and$3,226, respectively, in 2005 purchasing power parity terms. This relationship does not hold in the case of waste gas from production or solid waste. The estimation results from the model allowing region-specific slope coefficients show that the EKCs of the more developed coastal region have a flatter rising portion with turning points occurring at a higher income level than those of the less developed central and western regions. The paper argues that this may reflect technology diffusion and leapfrogging and institution imitation across regions at different stages of development. Policy implications of these findings are discussed.environmental kuznets curve; EKC; China; economic growth; environmental sustainability

Higher water tariffs for less river pollution—evidence from Min River and Fuzhou City, People’s Republic of China

Upstream nonpoint source pollution has become a significant threat to urban drinking water safety in the People’s Republic of China. Payment for environmental services (PES) is seen as a promising mechanism to deal with the situation. In designing a sound PES, it is crucial to determine the willingness to pay (WTP) of urban beneficiaries for upstream water pollution controls. An analysis of household data from a contingent valuation survey conducted in Fuzhou in 2009 reveals that household income is the most important factor in determining respondents’ positions on water tariff increases as well as WTP under a PES scheme. Mean WTP varies from Yuan (CNY) –0.45 per cubic meter to CNY0.86 for different income groups. The overall mean WTP is estimated to be CNY0.21, which is equivalent to a 10% increase in the current tariff, with the 95% confidence interval at (CNY0.12, CNY0.31). The point estimate implies a total annual WTP of Fuzhou City equal to CNY22 million, which is 27% less than the contribution of Fuzhou to an ongoing government-financed PES. However, with continuous water tariff increases, affordability among low-income households might arise as an issue. This calls for subsidies targeting low-income households to be incorporated in water tariff reform.water tariff, river pollution, upstream nonpoint source pollution, payment for environmental services, willingness to pay, Min River, Fuzhou City, China

Physically Plausible Spectral Reconstruction

Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly—i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera’s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases

Per-channel regularization for regression-based spectral reconstruction

Spectral reconstruction algorithms seek to recover spectra from RGB images. This estimation problem is often formulated as least-squares regression, and a Tikhonov regularization is generally incorporated, both to support stable estimation in the presence of noise and to prevent over-fitting. The degree of regularization is controlled by a single penalty-term parameter, which is often selected using the cross validation experimental methodology. In this paper, we generalize the simple regularization approach to admit a per-spectral-channel optimization setting, and a modified cross-validation procedure is developed. Experiments validate our method. Compared to the conventional regularization, our per-channel approach significantly improves the reconstruction accuracy at multiple spectral channels, by up to 17% increments for all the considered models

A Practical Study on Recovering Spectra from RGB Images

RGB cameras make three measurements of the light entering the camera, whereas hyperspectral imaging devices, per pixel, record the spectrum of the light. Spectral images have been shown to be more useful than RGB images in solving problems in many industrial application areas, including remote sensing and medical imaging. Spectral Reconstruction (SR) refers to a computational algorithm that recovers spectra from the RGB camera responses. This “make-the-RGBs-more-informative” process is most commonly implemented by machine learning (ML) algorithms, given matching RGB and hyperspectral data for training. Two mainstream ML approaches used in SR are regression and Deep Neural Network (DNN). While the former often has simple closed-form formulations for a pixel-based mapping, the latter approach is much more complicated: millions of parameters are used to map large image patches, in the hope that the network could utilise the spatial context in which each RGB is seen to further improve SR. It is generally accepted that regressions have long since been superseded by DNN methods. Nevertheless, few studies have actually been dedicated to comparing the two approaches. There are three main goals of this thesis. First, we benchmark regression- and DNN-based SR algorithms on the same hyperspectral image dataset. Here we pay close attention to the role that the spectral sensitivities of a camera play and also SR performance on unseen data. Second, we seek to improve regression-based algorithms and, in effect, attempt to close their gap in performance compared to DNN counterparts. Lastly, we investigate the practical issues faced by all SR algorithms. We consider SR performance as exposure changes and SR performance in a “closed-loop” imaging framework (i.e., do the spectra that an SR algorithm recovers integrate to the same input RGBs?). Our baseline benchmarking experiments indicate that the best DNN method only delivers a 12% accuracy improvement compared to the best-performing regression. Moreover, a regression method trained for one camera might actually outperform a DNN trained on another camera. Additionally, we find that the DNN’s worst-case performance (for unseen and unexpected scenes) is no better than the simplest regression method. Concomitantly, this encourages us to see if we could improve the average performance of regression methods. We propose three new improvements for regression methods. First, we reformulate the regressions so that they minimise a loss metric that is more similar to the one used to rank and train the leading DNN methods. Secondly, we revisit the regularisation step of the regression implementation. Regularisation is a technique for making the outputs of regressions more stable for unseen input and is usually governed by a single regularisation parameter. Here, we adopt as many regularisations as there are channels in a hyperspectral image, and this results in significant performance improvement. Lastly, we propose a new sparse regression framework. In sparse regression, we code RGBs in terms of the neighbourhood in the RGB space (via a clustering argument). We argue that this clustering is better performed in the spectral domain (where input RGBs are first regressed to some primary estimation of spectra). Combined, upgraded formulation and improved clustering, we develop a regression-based method found to work as well as the top DNN methods. As important as spectral accuracy is, trained SR algorithms need to work in practice, e.g., where objects and scenes can be viewed in varying exposure conditions. Unfortunately, we find that leading methods, such as non-linear regressions and DNNs, do not work well when exposure changes. Consequently, we propose new training frameworks which ensure the DNNs and regressions continue to work well under changing exposures. Finally, we investigate the following problem: we find that both regression- and DNNbased SR algorithms recover spectra that—when integrated with the camera’s spectral sensitivities—do not induce the same RGBs as the input to the algorithm. This means that the spectra that are recovered cannot (ever) be the correct spectra. Given this finding, we seek ways of adding physical plausibility (spectra should integrate to predict the input RGBs) to the SR algorithms. One of our proposed solutions is effectively a simple post-processing step which, provably, always improves the RMS (i.e., root-mean-square) performance of any SR algorithm

On the optimization of regression-based spectral reconstruction

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ 1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ 2 and ℓ 1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy

Higher water tariffs for less river pollution—evidence from Min River and Fuzhou City, People’s Republic of China

Upstream nonpoint source pollution has become a significant threat to urban drinking water safety in the People’s Republic of China. Payment for environmental services (PES) is seen as a promising mechanism to deal with the situation. In designing a sound PES, it is crucial to determine the willingness to pay (WTP) of urban beneficiaries for upstream water pollution controls. An analysis of household data from a contingent valuation survey conducted in Fuzhou in 2009 reveals that household income is the most important factor in determining respondents’ positions on water tariff increases as well as WTP under a PES scheme. Mean WTP varies from Yuan (CNY) –0.45 per cubic meter to CNY0.86 for different income groups. The overall mean WTP is estimated to be CNY0.21, which is equivalent to a 10% increase in the current tariff, with the 95% confidence interval at (CNY0.12, CNY0.31). The point estimate implies a total annual WTP of Fuzhou City equal to CNY22 million, which is 27% less than the contribution of Fuzhou to an ongoing government-financed PES. However, with continuous water tariff increases, affordability among low-income households might arise as an issue. This calls for subsidies targeting low-income households to be incorporated in water tariff reform

Environmental Kuznets Curves in the People’s Republic of China: turning points and regional differences

This paper examines the relationship between economic growth and environmental sustainability in the People’s Republic of China by empirically estimating environmental Kuznets curve (EKC) models using provincial-level panel data from 1985 to 2005. The results show that there exists an inverted-U shaped relationship as hypothesized by the EKC model between per capita income and per capita emissions (or discharges) in the cases of waste gas from fuel burning and waste water, with a turning point at per capita gross domestic product of $12,903 and$3,226, respectively, in 2005 purchasing power parity terms. This relationship does not hold in the case of waste gas from production or solid waste. The estimation results from the model allowing region-specific slope coefficients show that the EKCs of the more developed coastal region have a flatter rising portion with turning points occurring at a higher income level than those of the less developed central and western regions. The paper argues that this may reflect technology diffusion and leapfrogging and institution imitation across regions at different stages of development. Policy implications of these findings are discussed

Higher water tariffs for less river pollution—evidence from Min River and Fuzhou City, People’s Republic of China

Upstream nonpoint source pollution has become a significant threat to urban drinking water safety in the People’s Republic of China. Payment for environmental services (PES) is seen as a promising mechanism to deal with the situation. In designing a sound PES, it is crucial to determine the willingness to pay (WTP) of urban beneficiaries for upstream water pollution controls. An analysis of household data from a contingent valuation survey conducted in Fuzhou in 2009 reveals that household income is the most important factor in determining respondents’ positions on water tariff increases as well as WTP under a PES scheme. Mean WTP varies from Yuan (CNY) –0.45 per cubic meter to CNY0.86 for different income groups. The overall mean WTP is estimated to be CNY0.21, which is equivalent to a 10% increase in the current tariff, with the 95% confidence interval at (CNY0.12, CNY0.31). The point estimate implies a total annual WTP of Fuzhou City equal to CNY22 million, which is 27% less than the contribution of Fuzhou to an ongoing government-financed PES. However, with continuous water tariff increases, affordability among low-income households might arise as an issue. This calls for subsidies targeting low-income households to be incorporated in water tariff reform