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Green economic growth is the best alternative strategy for sustainable development. Existing literature investigated the determinants of green economic growth in China and provides mixed results. Thus, our study explores the impact of green environmental technology, financial innovation, and environmental regulations on green economic growth by controlling the impact of renewable energy consumption, trade, and education. The study explores the symmetric and asymmetric associations by employing ARDL and NARDL approaches. The ARDL long-run findings display that green environmental technologies, environmental regulations, and financial innovations positively and significantly contribute to green economic growth. However, the NARDL long-run findings infer that positive shock in green environmental technology, financial innovation, and environmental regulation exerts a significant and positive impact on green growth, while negative shock in green environmental technology, financial innovation, and environmental regulation have an insignificant impact on green growth. Based on the findings, the study delivers important policy implications to promote green economic growth in China.

The digital economy, which gradually emerged with a new generation of information technologies, has become an unavoidable reality for manufacturing firms in conducting green innovation activities. In this context, using matched panel data at the province and manufacturing firm levels in China during the period 2011-2019 as the sample, this article examines the nonlinear impact of the digital economy on firm green innovation, and further identifies the moderation mechanism of government quality and the heterogeneity of its effects. The two-way fixed-effects model reveals that there is not a simple linear association between the digital economy and firm green innovation as traditionally perceived, but rather an inverted U-shaped relationship that first promotes and then inhibits, which remains robust after applying endogenous and robustness tests. And most provinces have not yet crossed the inflection point; thus, the digital economy overall positively impacts green innovation. Further analysis shows that government quality positively moderates the relationship between the digital economy and firm green innovation, statistically reflecting that the turning point shifts upwards to the right under a higher-quality government. It is worth noting that, when heterogeneity in firm ownership, scale, and region is considered, the inverted U-shaped curve still exists, but the level of the digital economy at the inflection point differs, and the digital economy plays a greater role in promoting green innovation for state-owned, large-scale, or midwestern firms. This research has significant policy implications as it establishes an inverse U-shaped relationship between the digital economy and firm green innovation and indicates that while a firm's green patent output increases with the development of digitalization, it begins to decrease after a limit.

In this paper, we present a comparison study on three RLWE key exchange protocols: one from Ding et al. in 2012 (DING12) and two from Alkim et al. in 2016 (NewHope and NewHope-Simple). We compare and analyze protocol construction, notion of designing and realizing key exchange, signal computation, error reconciliation and cost of these three protocols. We show that NewHope and NewHope-Simple share very similar notion as DING12 in the sense that NewHope series also send small additional bits with small size (i.e. signal) to assist error reconciliation, where this idea was first practically proposed in DING12. We believe that DING12 is the first work that presented complete LWE & RLWE-based key exchange constructions. The idea of sending additional information in order to realize error reconciliation and key exchange in NewHope and NewHope-Simple remain the same as DING12, despite concrete approaches to compute signal and reconcile error are not the same.

Ring Learning With Errors (RLWE)-based key exchange is one of the most efficient and secure primitive for post-quantum cryptography. One common approach to achieve key exchange over RLWE is error reconciliation. Recently, an efficient attack against reconciliation-based RLWE key exchange protocols with reused keys was proposed. This attack can recover a long-term private key if a key pair is reused. We also know that in the real world, key reuse is commonly adopted in applications like the Transport Layer Security (TLS) protocol to improve performance. Directly motivated by this attack, we construct a new randomized RLWE-based key exchange protocol against this attack. Our lightweight approach incorporates an additional ephemeral public error term into key exchange materials, so that this attack no longer works. With the same attack, we practically show that the signal value of our protocol is indistinguishable from uniform random, therefore, this attack no longer works. We explain how the attack fails, present 200-bit classic and 80-bit quantum secure parameter choice, efficient implementations, comparisons and discussion. Benchmark shows our protocol is truly efficient and even faster than related vulnerable protocols.

A highly efficient, rapid, green and safe procedure for the preparation of free all-trans-astaxanthin from Haematococcus pluvialis algal extract, by a crude extracellular enzyme extract, was reported. The free all-trans-astaxanthin obtained by the biocatalysed method had fewer side products compared to the saponification procedure. Through single-factor experiments and a Box-Behnken design, it was possible to find the optimal biocatalytic conditions for the hydrolysis of 2 mg of H. pluvialis oil with 14.7 mg (protein content) of lyophilised crude extracellular enzyme extract obtained from Pseudomonas aeruginosa. The reaction was carried out in 30 min at pH 9.16 and 36 °C, in 5.5 mL total reaction volume, under nitrogen atmosphere and dark conditions. The hydrolysis ratio of the astaxanthin esters was 98.72%, and the production of free all-trans-astaxanthin was 82.83 ?g per mg of H. pluvialis oil. The method herein reported was simpler than other enzymatic methods previously described and allowed saving of time and costs.

A thermostable and alkaline-stable novel esterase (Est7) was identified through the whole genome sequencing of Stenotrophomonas maltophilia OUC_Est10. The open reading frame of this gene encoded 617 amino acid residues. After heterologous expression in Escherichia coli BL21 (DE3), the purified Est7 was separated as a single protein and presented a molecular mass of 70.6 kDa. Multiple sequence alignment indicated that Est7 had a typical catalytic triad (Ser-Asp-His) and the conserved sequence (GDSL) typical of the family II lipid hydrolase proteins. Est7 showed good stability in alkaline buffers, especially in Tris-HCl buffer at pH 9.0 (residual activity 93.8% after 96 h at 4 &deg, C) and in the medium temperature conditions (residual activity 70.2% after 96 h at 45 &deg, C and pH 8.0). The enzyme also retained higher stability toward several hydrophilic and hydrophobic organic solvents (e.g., after incubation in 100% acetonitrile or in n-hexane the enzyme retained about 97% and 84% of the activity in the absence of organic solvent, respectively). Furthermore, Est7 could catalyze the transesterification reaction of vinylacetate with 2-phenylethanol and cis-3-hexen-1-ol to their corresponding acetate esters in petroleum ether or tert-butyl methyl ether. These results indicate Est7 as a promising biocatalyst for applications of Est7 in non-aqueous media.

Air pollution originating from anthropogenic emission, which is an important factor for environmental policy to regulate the sustainable development of enterprises and the environment. However, the missing or mislabeled discharge data make it impossible to apply this strategy in practice. In order to solve this challenge, we firstly discover that the energy consumption in a factory and the air pollutants are linearly related. Given this observation, we propose a support vector regression based Single-location recovery model to recover the air pollutant emission by using the energy consumption data in a factory. To further improve the precision of air pollutant emission estimation, we proposed a Gaussian process regression based multiple-location recovery model to estimate and recover the missing or mislabeled air pollutant emission from surrounding available air quality readings, collected by the government’s air quality monitoring station. Moreover, we optimally combine the two approaches to achieve the accurate air air pollutant emission estimation. To our best of knowledge, this is the first paper for monitoring the air pollutant emission taking both a factory’s energy consumption and government’s air quality readings into account. The research model in this article uses actual data(10,406,880 entries of data including weather, PM 2.5, date, etc.) from parts of Shandong Province, China. The dataset contains 33 factories (5 types) and we use the co-located air quality monitoring station as ground truth. The results show that, our proposed single-location recovery, multi-location recovery, and combined method could acquire the mean absolute error of 8.45, 9.69, and 7.25, respectively. The method has consistent accurate prediction behavior among 5 different factory types, shows a promising potential to be applied in broader locations and application areas, and outperforms the existing spatial interpolation based methods by 43.8%.

Astaxanthin is a pigment with various functions. Free astaxanthin is obtained mainly through saponification methods, which could result in many byproducts. Enzymatic methods using lipases have been used in a few cases, while there are no reports on the use of esterases for the production of free astaxanthin. Herein we present the screening and identification of a novel esterase (Est3-14) from a marine mud metagenomic library. Est3-14 is pH-sensitive and keeps good stability in alkaline buffers (residual activity 94%, pH 8.0, 4 °C, and 36 h). Meanwhile, Est3-14 keeps a good stability in the medium temperature condition (residual activity 56.7%, pH 8.0, 40 °C, and 84 h). Est3-14 displayed high hydrolysis activity to prepare free all- trans-astaxanthin in biphasic systems. Furthermore, under optimal conditions (0.5 mL ethanol, 6 mL 0.1 M Tris-HCl buffer, pH 8.0, 0.5% (w/v) H. pluvialis oil, 40 °C), the hydrolytic conversion ratio was 99.3% after 36 h.