Search Results (8)

Deploying neural networks on encrypted data requires efficient evaluation of nonlinear activations, especially the ReLU function, without decryption. While the CKKS homomorphic encryption scheme supports packed arithmetic over approximate numbers efficiently, its approximate semantics make direct nonlinear evaluation difficult, and polynomial surrogates often introduce approximation error and non-discrete outputs. In this work, we present a task-specific, non-interactive construction for discrete ReLU evaluation in CKKS by combining modulus-switch-based discretization with interpolation-driven lookup-table (LUT) evaluation. We instantiate this design in two complementary schemes. The first uses trigonometric Hermite interpolation and functional bootstrapping to compute a discrete sign indicator, which is then combined with the encrypted input through conditional multiplication to obtain the ReLU output; this variant is compact and suitable for lightweight settings. The second uses iterative most-significant-bit (MSB) bootstrapping to support larger plaintext moduli and higher-precision regimes through repeated digit extraction. A common enabler of both schemes is a discretization step that maps approximate CKKS plaintexts to a finite integer representation; exactness in our setting therefore refers to exact evaluation over this discretized representation, while the deviation from the original CKKS plaintext is governed by the discretization error analyzed in Lemma 1. Experiments on encrypted MNIST inference and the accompanying LUT/storage analysis indicate that the proposed schemes preserve competitive accuracy relative to polynomial-approximation baselines while maintaining manageable auxiliary storage under the reported parameter settings. These results suggest that interpolation-based discrete activation is a promising alternative to polynomial approximation for selected CKKS-based encrypted inference tasks. Full article

The carbon tax is a crucial economic instrument for China; it aims to encourage the reduction of carbon emissions and provide additional revenue for the government in order to promote the transformation of society towards low-carbon and sustainable development. The suboptimal carbon tax refers to the carbon tax rate that achieves the best balance between emission reduction targets and economic benefits. Using China’s 2020 Non-competitive Input–Output Table, which encompasses 42 sectors, alongside carbon emission data sourced from the China Carbon Emission Accounts and Datasets (CEADs) covering 47 sectors, this study established a Carbon Tax-adjusted Input–Output Table of China’s Non-competitive Carbon Emissions 2020 (26 sectors) and constructed a multi-objective suboptimal carbon tax model based on an input–output price change model. Based on these, the suboptimal carbon tax rates under four different sets of constraints were simulated, including 49.2 CNY/ton (low inflation), 98.3 CNY/ton (low-to-medium inflation), 147.1 CNY/ton (medium-to-high inflation), and 195.5 CNY/ton (high inflation). We found that the suboptimal carbon tax should take into account its impact on prices, carbon reduction, and GDP, and higher carbon tax rates lead to more significant macroeconomic impacts and increased efforts in reducing emissions. Policy recommendations have also been put forward, such as launching a comprehensive research framework, establishing a synergistic and complementary mechanism between carbon taxation and carbon trading, designing a dynamic carbon tax, etc. Full article

Carbon tax is an important economic instrument in achieving the goal of carbon emission reduction and sustainable development. This paper investigates the effects of carbon tax on carbon emission reduction in China. First, a non-competitive input–output table for Carbon Emissions of 28 sectors in China after Carbon Tax was established, based on the “2018 China Non-competitive Input–Output Table (42 Sectors)” and the carbon emission data of sectors provided by China Carbon Emission Accounts and Datasets (CEADs). Then, an input–output price model was established to study the changes on product price, GDP, employment, and carbon dioxide emissions of 28 sectors after carbon taxing ranged from 10 to 200. When the carbon tax rate reaches 200 yuan/ton, the inflation rate will be 5.907%, the total GDP will be decreased to 1.910%, the total labor force will be decreased to 1.744%, and the total carbon emission reduction rate will be increased to 8.171%. Results showed that with the increases in carbon tax, the inflation rate was increased, the rate of carbon emission reduction was increased, and the negative effects on GDP and employment were also increased. Suggestions on policy making, such as combination of carbon taxing and carbon trading, dynamic adjustment mechanism, tax neutrality, and forcing active carbon reduction, were proposed to minimize the adverse effect of levying carbon tax. The results from this paper would provide a reference for the policy making on carbon management. Full article

Firstly, based on the data of China’s noncompetitive input–output tables from 2002 to 2017, this paper comprehensively grasps the integration trend of China’s agriculture-related industries and digital economy industries by constructing integration contribution and interaction indicators. Secondly, the correlation between the two industries is analyzed more intuitively with the help of the APL (Average Propagation Length) model. Finally, it analyzes the coordination of the digital economy industry and the transformation of the agriculture industry with the help of the grey correlation method. The results show that the digital economy industries contribute the most in absolute terms, compared to other industries, to the agro-processing industry and have the highest degree of integration and interaction with the agricultural product transportation and marketing industry. In terms of the breakdown of the digital economy industries, digital product manufacturing is weakly linked to agriculture-related industries, but the coordination among the transformation and upgrading of agriculture is gradually improving. Compared to digital product manufacturing, the digital technology application industry has a higher direct contribution to the agriculture-related industry. After 2012, the overall driving and pulling effect on the agriculture-related industry is basically the same, and the coordination with agricultural transformation and upgrading is optimal. The contribution of the digital factor-driven industry to agriculture-related industries experienced an explosive period after 2012, with the highest overall contribution and strongest industrial linkage between the two, while coordination with the transformation and upgrading of agriculture decreases. Therefore, in combination with the No. 1 document of the Central Government in 2022, which specifically calls for the implementation of the “digital business to promote agriculture” project and the promotion of e-commerce in the countryside, this paper puts forward suggestions to give full play to the role of digital technology in the sustainable development of agriculture-related industries. Full article

While promoting economic growth, industrial development is causing serious environmental problems and threatening human health. Studies on pollution transfer through international trade often over-estimate the actual embodied emissions in exports and ignore the industrial pollutants. By designing a non-competitive input-output model which differentiates between processing exports and normal exports, we calculate the embodied domestic and imported industrial emissions in China’s processing and normal exports and imports. We also calculate the balance of embodied emission in trade (BEET) and the pollution terms of trade (PTT), as well as the decomposition of scale, structural, and technical effects on embodied emission in international trade. The results demonstrate that processing exports reduce domestic pollution by importing intermediate inputs; normal exports, on the other hand, have a considerable impact on domestic pollution. Bilateral trade between China and the US has the most detrimental impact on China’s local environment, followed by trade between China and Japan. China’s exports to Japan are more polluting per unit than those to the US and Germany. Technological upgradations and transformation of trade structure have helped to reduce the negative environmental consequences of China-US and China-Japan bilateral trade. Investment in technology and trade policy can lead to a cleaner production ecosystem. Full article

An accurate and practically useful evaluation of regional water scarcity is a necessary procedure in scarcity monitoring and threat mitigation. From the perspective of virtual water, this study proposed an improved system dynamics model to evaluate regional water scarcity (WS), including a case study of Henan province, China. We enhanced the existing system dynamics model of WS evaluation from a virtual water perspective by (1) defining WS as the ratio of the consumption-based blue water footprint to water availability, in order to compare the water requirements that need to be met to satisfy the local demand of goods and services with water supply; (2) integrating the economic growth, trade, and water use efficiency in the tertiary industry (e.g., accommodation, food and beverage services) into the model, in order to improve the accuracy of WS assessment and help find more specific measures to reduce WS by factor adjustment; (3) distinguishing the product use structure matrix, as well as the sectoral direct water use coefficient, in local regions from that in other domestic regions and foreign countries, and identifying the regional use structure matrices of products from these three kinds of regions, in order to increase the calculating veracity; and (4) displaying performances of the society, economy, and environment in WS reduction, in order to offer a more comprehensive reference for practical policy decisions. The case study results show that Henan has been suffering from, and in the near future could continue to face, water scarcity, with an average of 2.19 and an annual rise of 1.37% during 2008–2030. In the scenario comparison of current development, production structure adjustment, technology upgrade, and trade structure adjustment in supply-side structural reform of Henan from 2019 to 2030, WS could be reduced by updating production structures into less production of agricultural products or other sectors with a high production-based water footprint (with the smallest average WS of 2.02 and the second smallest total population and GDP, i.e., gross domestic production), technology enhancement in water saving, purification and pollution control (with the second smallest average WS of 2.04 and the largest total population, GDP and total available water resources). Furthermore, for the agricultural products or other sectors with high domestic/international virtual water outflow (inflow), if we reduce (increase) their percentage of outflow (inflow) in the industry involved, WS will increase only more slightly than that when we keep the current development trend, with the smallest total population. Potential measures for alleviating WS should be taken comprehensively, with priorities being identified according to the socioeconomic and environmental performance. Our model can be useful for practical policymaking and valuable for relevant research worldwide. Full article

Based on the national input–output table, a comparable price non-competitive input–output table was compiled for 2002, 2007, and 2012. The influence factors of price and product imports were removed from the table. Furthermore, a water-use input–output table was constructed based on the links between the economic system and water resources management. With the multi-factor structural decomposition analysis (SDA) model developed in this paper, the driving forces of water use were decomposed into 18 factors, and quantitative effect results were obtained. Total water use in China increased by 3.9% from 2002 to 2007 and by 5.4% from 2007 to 2012 with the combined effects of multiple factors. For example, the increase in economic scale raised water use by 46.6% and 45.5%, respectively. Advancement in agricultural technology (production and water-saving technologies) reduced water use by 14.9% and 19.8%, respectively. Reducing the proportion of thermal/nuclear power and increasing the price of electricity have water use-reducing effects. Changes in the mode of development considerably reduced water use by 9.5% and 5.3%, respectively. Water-use management should focus on factors that have great influence on water use and show high water-use sensitivity. Full article

With the fast-paced urbanization process, there will be a rapid development of China’s construction industry. However, it could also drive for considerable energy consumption, resulting in immense pressure on the environment. Based on non-competitive (import) input–output tables, we employed the SRIO (Single Region Input–Output) model to analyze energy use embodied in China’s construction industry from 1995 to 2009 and made projections for 2020. Our results show that about 4.84 billion tons of coal equivalent energy would be consumed by China’s construction industry in 2020. It implies that urbanization not only promotes the flow of embodied energy, but also provides development opportunities for a green and energy-saving construction industry. Full article