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This study proposes low-carbon technology (LCT) solutions from the perspective of incremental cost-effectiveness and public satisfaction based on calculating carbon emissions and economic costs.

According to the citation frequency, 11 indicators of low-carbon neighborhood (LCN) were selected so as to construct the low-carbon renewal potential evaluation model. Five neighborhoods were selected to evaluate low-carbon renewal potential based on the driving-pressure-state-impact-response (DPSIR). Moreover, the neighborhoods with the highest renewal potential were selected for further analysis. Then, the feasibility decision was carried out among seven typical LCTs based on the value engineering (VE) method. Finally, the TOPSIS method was applied to calculate the public satisfaction and demand so as to get the priorities of these LCTs. Through comprehensive analysis, the final LCT solutions could be carried out.

Our practice proves that the evaluation model combined with the decision-making methods can provide scientific decision-making support for the LCT solutions. Some LCTs perform consistently across different neighborhoods by comparing VE results and TOPSIS rankings. The solar photovoltaic (PV) (T3) has high value and significant attention which gives it a top priority for development, while the energy-efficient windows and doors (T2) have relatively low value.

There is a lack of research that considers the economic cost, low-carbon efficiency and public satisfaction when proposing LCT solutions for neighborhood renewal projects. Faced with the problem, we practice the decision-making from two dimensions, that is, the “feasibility decision with VE” and the “priorities decision with TOPSIS.” In this way, a balance between incremental cost-effectiveness and public satisfaction is achieved, and LCT solutions are proposed.

Because of advantages such as multi‐scale and multi‐direction, contourlet transform is better at treating a 2‐D image than wavelet transform and the Classified Hidden Markov Tree (CHMT) model is able to analyze statistical character of coefficients, which means they can be analyzed more efficiently and effectively. So in this paper, the purpose is to use contourlet transform and CHMT model to redevelop traditional set partitioning in hierarchical trees (SPIHT) code algorithm, and by using these two methods, hope to decrease the distortion and increase the quality.

In this paper, the algorithm is divided into two parts: code part and decode part. As all processes are operated in the contourlet domain, contourlet transform is finished at the beginning of the code part. SPIHT algorithm in the contourlet domain will code these contourlet coefficients. CHMT model will be built in decode part, it will optimize decoded coefficients by calculating their statistical relationship. The decoded image will be reconstructed.

The experiment proves that this algorithm is able to reduce the distortion under the premise of not affect compression rate, meanwhile peak signal‐to‐noise ratio value is improved compared with traditional methods. Furthermore, the visual effect of the image derived from the algorithm is superior to that derived by traditional methods.

CHMT model in contourlet domain used in image code is original. As an improved code algorithm, the method is more powerful; its use could decrease the distortion of decoded images invaluable in the fields of military, medical image analysis, deep space detection and so on.