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Ticks rank high among arthropod vectors in terms of numbers of infectious agents that they transmit to humans, including Lyme disease, Rocky Mountain spotted fever, Colorado tick fever, human monocytic ehrlichiosis, tularemia, and human granulocytic anaplasmosis. Increasing temperature is suspected to affect tick biting rates and pathogen developmental rates, thereby potentially increasing risk for disease incidence. Tick distributions respond to climate change, but how their geographic ranges will shift in future decades and how those shifts may translate into changes in disease incidence remain unclear. In this study, we have assembled correlative ecological niche models for eight tick species of medical or veterinary importance in North America (Ixodes scapularis, I. pacificus, I. cookei, Dermacentor variabilis, D. andersoni, Amblyomma americanum, A. maculatum, and Rhipicephalus sanguineus), assessing the distributional potential of each under both present and future climatic conditions. Our goal was to assess whether and how species’ distributions will likely shift in coming decades in response to climate change. We interpret these patterns in terms of likely implications for tick-associated diseases in North America.

Reducing transportation CO 2 emissions and addressing population characteristic changes are two major challenges facing China, involving various requirements for sustainable economic development. Due to the interdependence of population characteristics and transportation, human activities have become a significant cause of the increase in greenhouse gas levels. Previous studies mainly focused on evaluating the relationship between one-dimensional or multi-dimensional demographic factors and CO 2 emissions, while few studies have reported on the effect of multi-dimensional demographic factors on CO 2 emissions in transportation. Analyzing the relationship between transportation CO 2 emissions is the foundation and key to understanding and reducing overall CO 2 emissions. Therefore, this paper used the STIRPAT model and panel data from 2000 to 2019 to investigate the effect of population characteristics on CO 2 emissions of China’s transportation sector, and further analyzed the effect mechanism and emission effect of population aging on transportation CO 2 emissions. The results show that (1) population aging and population quality restrained CO 2 emissions from transportation, but the negative effects of population aging were indirectly caused by economic growth and transportation demand. And with the aggravation of population aging, the influence on transport CO 2 emissions changed and presented a U-shape. (2) Population living standard on transportation CO 2 emissions exhibited an urban–rural difference, and urban living standard was predominant in transportation CO 2 emissions. Additionally, population growth is under a weakly positive effect on transportation CO 2 emissions. (3) At the regional level, the effect of population aging on transportation CO 2 emissions showed regional differences. In the eastern region, the CO 2 emission coefficient of transportation was 0.0378, but not significant. In central and western regions, the influence coefficient of transportation was 0.6539 and 0.2760, respectively. These findings indicated that policy makers should make relevant recommendations from the perspective of coordinating population policy and energy conservation and emission reduction policy in transportation.

Grass carp ( Ctenopharyngodon idellus ) is the most productive freshwater fish in China, but its traditional aquaculture model still has problems, such as poor water quality and frequent diseases. We have taken monoculture and 80:20 polyculture grass carp ponds as the research object and used EwE software to build the Ecopath model of two ponds. We analyzed and compared the characteristics of ecological structure and energy flow in two ponds. The result showed the highest effective trophic level in the polyculture pond that was higher than that in the monoculture pond, and fish in polyculture had higher EE values which showed the production of fish in polyculture contributed more to the energy conversion efficiency of the ecosystem. Flows into detritus were the largest component of TST both in the two ponds, which accounted for 49.34% and 50.37%. And the average transfer efficiency in monoculture was 13.07%, while that in polyculture was 15.6%. The ascendency/total development capacity (A/TDC) and overhead/total development capacity (O/TDC) were 0.35 and 0.65 both in the two ponds, respectively, which indicated that both systems had a strong anti-perturbation ability, but the stability could be improved. Finn’s cycling index (FCI) in polyculture was higher and showed that the polyculture pond was more mature and stable. Unused energy of functional groups will flow to detritus, and that in the monoculture pond was higher, the energy of C. idellus that flowed to detritus in monoculture was 48.17% higher than that in polyculture; unused energy of bacteria and phytoplankton were also high. The result showed that polyculture could improve energy utilization, increase transfer efficiency, and raise the stability of the ecosystem. Grass carp ponds still need to be improved in the aspects of mixed species and energy consumption. It is necessary to improve the ecological and economic benefits of grass carp ponds by optimizing the aquaculture structure and adjusting the aquaculture proportion.

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders’ birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions’ carbon intensities are increased due to fiscal rivalry.

General purpose technologies (GPTs) like AI enable and require significant complementary investments. These investments are often intangible and poorly measured in national accounts. We develop a model that shows how this can lead to underestimation of productivity growth in a new GPTs early years and, later, when the benefits of intangible investments are harvested, productivity growth overestimation. We call this phenomenon the Productivity J-curve. We apply our method to US data and find that adjusting for intangibles related to computer hardware and software yields a TFP level that is 15.9 percent higher than official measures by the end of 2017.

The carbon emission trading pilot policy is an important initiative to achieve synergistic economic-environmental development. Based on the data of 268 cities in China from 2006 to 2020, this paper analyzes the impact of carbon emission trading pilot policy on urban innovation capacity by using a time-varying difference-in-difference model. The study shows that, first, the implementation of the CETP improves the innovation capacity of cities, and the robustness test confirms the above findings. Second, the effect of the policy on enhancing urban innovation capacity is heterogeneous between the type of innovation and city type: the promotion of innovation capacity is stronger for utility model patents and non-capital cities. Third, there is a positive spillover effect of the implementation of the CETP on the promotion effect of the urban innovation capacity, which can lead to the improvement of the innovation capacity of neighboring cities. This paper has some reference value for building a unified carbon emission trading market and promoting low-carbon economic development within China.

In this paper, we review the literature on declining business dynamism and its implications in the United States and propose a unifying theory to analyze the symptoms and the potential causes of this decline. We first highlight 10 pronounced stylized facts related to declining business dynamism documented in the literature and discuss some of the existing attempts to explain them. We then describe a theoretical framework of endogenous markups, innovation, and competition that can potentially speak to all of these facts jointly. We next explore some theoretical predictions of this framework, which are shaped by two interacting forces: a composition effect that determines the market concentration and an incentive effect that determines how firms respond to a given concentration in the economy. The results highlight that a decline in knowledge diffusion between frontier and laggard firms could be a significant driver of empirical trends observed in the data. This study emphasizes the potential of growth theory for the analysis of factors behind declining business dynamism and the need for further investigation in this direction.

Green bonds, a new green financial instrument, encourage enterprises to achieve high-quality development through green technology innovation. However, a lack of research is currently being conducted into the effect of green bond issuance in China. Can green bonds effectively empower enterprises to green innovation? What is the underlying mechanism? In the context of carbon-neutral strategies, it is significant to answer these questions scientifically. This paper uses a quasi-natural experiment of the launch of the green bond market in China in 2016 to conduct empirical studies based on the panel data of 1 558 non-financial Chinese-listed enterprises from 2015 to 2020 with the multi-period difference-in-difference model. The results show that ① issuing green bonds can significantly empower enterprises’ green technology innovation. The empowering effect is mainly for green utility patents rather than green invention patents. This result remains after dynamic heterogeneity analysis, placebo test, and other tests. In addition, the effect has a lag. ② Heterogeneity tests show that this empowerment effect varies across enterprises with different property rights, industries, and regions. ③ In terms of the mechanism of action, green bonds can enhance enterprises’ ability to innovate green technology by increasing the proportion of long-term loans and improving their debt structure. This paper broadens the relevant literature on the economic consequences of green bonds and the influencing factors of enterprises’ green technology innovation and provides policy suggestions for further improving the analysis of green bonds.

Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered “black boxes,” making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread. Plain language summary Artificial intelligence in drug development: reshaping the therapeutic landscape The pharmaceutical industry has enormous and growing amounts of data, and in terms of models, the best AI pharma model is not to build pure AI processes. Combining humans and AI is often superior to human processes or AI processes alone. Just as in chess, the combination of a human and a computer algorithm can usually beat a human or a computer algorithm alone. AI technology methods need to be sorted out and developed. AI’s attention, exploration, and application trials in all sectors of society will inevitably accelerate the maturation and innovation of AI technology methods. When the logic of the “large data → more accurate models → better drugs → more and better data” cycle matures in practice, AI pharma will be significantly accelerated. However, the application and diffusion of any technology are challenging to achieve overnight, and it is the law of development that new things spiral and move in waves. AI and data-driven pharma models need to be explored and practiced more and more before they can truly demonstrate their value.

This paper explores the impact of population agglomeration on urban economic development quality in various cities of China. The results show that population agglomeration significantly contributes to the improvement of urban green total factor productivity by increasing population diversification, promoting knowledge spillovers, and reducing pollution emission intensity. Moreover, we find that population agglomeration in type II big cities and type I large cities significantly improves green total factor productivity, while the impact of population agglomeration in metropolises and mega-cities on green total factor productivity is not significant. On the one hand, type II big cities and type I large cities are in the period of rising economic development, the population has not yet reached saturation, and there is still a large demographic dividend space. On the other hand, excessive population agglomeration also brings about “urban diseases” such as population congestion and traffic congestion, especially in the metropolises and mega-cities. Finally, using data on producer services and its sub-sectors, we identify a more significant driving effect of high-end talent agglomeration on green total factor productivity.