The mine ecological restoration fund system is a key task in the construction of ecological civilization. However, the existing accounting system, basic framework, and process handling are insufficient to meet the market-oriented demands of the mine ecological restoration fund. Based on this, this paper, using a decentralized logic approach, designs a decentralized management model for the mine restoration fund, incorporating’s classified financing-blocked usage layered management. This model explores the accounting elements of the fund, and based on the characteristics of fund management and flow, accounts for fund income, expenditure, and termination stages, presenting a comprehensive view of the fund's flow process. This paper explores the design of the mine ecological restoration fund's accounting system and the optimization of its funding sources, providing theoretical and empirical support for the accounting of the fund by mine enterprises, government departments, and social investors, and contributes to the improvement of the mine ecological restoration fund system framework.
The non-fungible token (NFT) is an emergent type of cryptocurrency that has garnered extensive attention since its inception. The uniqueness, indivisibility, and humanistic value of NFTs are the key characteristics that distinguish them from traditional tokens. The market capitalization of NFT reached 21.5 billion USD in 2021, almost 200 times that of all previous transactions. However, the subsequent rapid decline in NFT market fever in the second quarter of 2022 casts doubt on the ostensible boom in the NFT market. To date, there has been no comprehensive and systematic study of the NFT trade market or of the NFT bubble and hype phenomenon. To address this gap, we conduct an in-depth investigation of the entire Ethereum ERC721 and ERC1155 NFT ecosystems by dividing the NFT ecosystem participants into three categories: creators, transferors, and holders, to understand their manipulations of NFTs and infer their intentions. We examine the differences between NFT and ERC20 traders and then analyze the possible reasons behind these differences, leading us to gain insights into the varying degrees of market bubble associated with the two kinds of tokens. Through the construction and analysis of the NFT transfer graph (NTG), we uncover certain anomalous behaviors related to bubbles, along with quantifying the proportion of these anomalous behaviors, to reveal the degree of bubbles within the entire ecosystem.
Simone Fatichi, Sebastian Leuzinger, Christian Körner
Knowledge of future terrestrial carbon (C) pools and fluxes is based on simulations by Dynamic Global Vegetation Models (DGVMs; Cox et al., 2000; Sitch et al., 2008). For simplicity we used the DGVMs acronym to include all of the models able to simulate C and vegetation dynamics at large spatial scales, which are sometimes referred to as Terrestrial Biosphere Models, Terrestrial Ecosystem Models, vegetation components of Earth System Models, and Land Ecosystem Models. DGVMs are now typically coupled to climate models to account for biophysical and biogeochemical feedback caused by vegetation (Bonan, 2008; Chapin et al., 2008; Anderson et al., 2011). The ultimate aim is to simulate climate–vegetation dynamics that explicitly account for mutual interactions and thus provide us with a better spatiotemporal description of water fluxes together with the most realistic scenarios for the future climate and C cycle (Friedlingstein et al., 2006; Thornton et al., 2007; Bonan et al., 2011). Current DGVMs are simulating long-term tree and forest stand growth as a consequence of the amount of assimilated C, triggering an inevitable positive feedback between C assimilation and growth. The key factors affecting stomatal aperture and C assimilation are atmospheric CO2 concentration, water availability, light, vapor pressure deficit and temperature (Sellers et al., 1997; Lawson et al., 2011). However, direct control of C sinks (defined as growth in the sense of C investment on plant tissue expansion) via environmental factors has been shown to be more important than indirect control via photosynthesis (the C source, see Fig. 1). For example, water- or temperature-limited plants tend to reduce growth but increase C storage (Körner, 2003; Sala & Hoch, 2009; Woodruff & Meinzer, 2011; Sala et al., 2012), which suggests that environmental controls act first on sink activity rather than source activity (Körner, 2013). Nevertheless, in all existing DGVMs, plant growth is driven by photosynthesis directly without considering water and thermal limitations via metabolic, cambial and meristematic activity (blue arrows in Fig. 1; Bonan et al., 2003; Sitch et al., 2003; Krinner et al., 2005). The entire photosynthesized net C (the source) is then partitioned among different C pools, mostly based on allometric rules derived from observations (Poorter et al., 2012) or using simplified functional allocation schemes (Friedlingstein et al., 1998; Franklin et al., 2012). Allocation to carbohydrate reserves, root exudates and export to symbionts are mostly missing. This opens up a huge discrepancy between the way plant growth is modeled today (blue arrows in Fig. 1) and the way it is understood based on experimental evidence (red arrows in Fig. 1). In this article, we suggest a revised hierarchy of plant growth control by removing the causal link from C assimilation to plant growth and by providing a description of the mechanistic connections among processes. Available soil water, temperature, nutrients, light, and CO2 are indisputably the key drivers of plant growth (Boisvenue & Running, 2006; McMurtrie et al., 2008; Ågren et al., 2012; Fig. 1). The former three (water, temperature, and nutrients) are fundamentally different from the latter two (light and CO2) because they can affect both sink and source activities, while light and CO2 only affect the source activity (C assimilation, Fig. 1). In this section, we compare the hierarchy of such limitations based on experimental evidence, which will lead into the discussion of strategies for modeling plant growth. When water limitations occur, there is evidence that cambial and leaf growth are inhibited at much lower levels of water stress (higher water potentials) than photosynthesis (Fig. 2; Boyer, 1970; Hsiao, 1973; Hsiao et al., 1976; Muller et al., 2011; Tardieu et al., 2011). Because organ expansion is affected earlier and more intensively than photosynthesis, plants experiencing soil water deficit often accumulate nonstructural carbohydrates (NSC) and reduce growth (Würth et al., 2005; Woodruff & Meinzer, 2011). Drought stressed or more apical parts of trees show lower xylem- or leaf-water potential than well-watered or more basal parts. This decrease in xylem- or leaf-water potential implies a reduction in cell turgor and in the capacity to transport sugars (Woodruff et al., 2004; Sala et al., 2011; Woodruff & Meinzer, 2011). Specifically, lower cell turgor has the potential to limit cell wall expansion, cell wall synthesis and protein synthesis (Lockhart, 1965; Hsiao, 1973; Sala et al., 2011). This means that C assimilation continues while sink activity (tissue growth) is inhibited, which most likely explains the accumulation of NSC in stressed plants (Körner, 2003). For example, Sala & Hoch (2009) showed that in Pinus ponderosa, mobile C compounds increase with increasing tree height. The possibility of plants actively prioritizing storage over growth allows additional interpretations of the role of accumulating NSC, for example, to maintain the integrity of the hydraulic system (Sala et al., 2012) or for signaling purposes (Rolland et al., 2006). However, recent evidence seems to support that when hydraulic transport is not affected, NSC is significantly depleted as a consequence of C demand (Hartmann et al., 2013; Sevanto et al., 2013). Exacerbation of these mechanisms (low turgor and incapability to transport sugars) also seems to play an important role in tree mortality and therefore long-term forest dynamics (McDowell, 2011; McDowell et al., 2011). Inhibition of sink activity via low water potentials and the resulting accumulation of photo-assimilates in leaves can also lead to direct feedback, that is downregulating photosynthesis (Paul & Foyer, 2001; Nikinmaa et al., 2013), which demonstrates a direct control of photosynthesis via growth (red horizontal arrow in Fig. 1). Similarly to water stressed plants, temperature-limited plants such as trees at the treeline and winter crops are typically limited by sink activity (tissue expansion) earlier than by source activity (C assimilation; Fig. 1; Körner, 2012). Consequently, cold-limited plants show an increase rather than a decrease in NSC with colder temperatures (Körner, 2008; Fajardo et al., 2012; Hoch & Körner, 2012). Temperature influences several metabolic processes (e.g. cell doubling time), determining the potential growth rate of organs in the absence of other growth limiting factors (Pantin et al., 2012). Most temperature-controlled processes of plant growth have been summarized by Boltzmann–Arrhenius type equations, which describe a decrease in growth rates at sub-optimal and supra-optimal temperatures (Parent et al., 2010). Furthermore, a 5–6°C threshold has often been identified to limit growth in cold adapted species, irrespective of photosynthetic activity which typically ceases only at freezing point (Körner, 2008). Nutrient limitation is well known to exert a negative feedback on photosynthesis via the amount of fundamental enzymes needed for C assimilation that can be produced. The amount of synthesized Rubisco, for example, is strongly controlled by nitrogen availability (Kattge et al., 2009). However, nutrients also act as direct plant growth control due to the relatively constant stoichiometry of plant tissue composition (Sterner et al., 2002; Leuzinger & Hättenschwiler, 2013). This has emerged clearly from FACE experiments where progressive nitrogen limitation (Luo et al., 2004) has been demonstrated to limit plant growth (Norby et al., 2010). While plants have the capacity to recruit additional nutrients by expanding their root system and via mycorrhizal symbioses, nutrient limitations are likely to progressively emerge at the landscape scale. Importantly, no study has been able to clearly quantify nutrient limitation acting on sink (tissue growth) vs source activity (C assimilation). Light as well as atmospheric CO2 clearly limit photosynthesis (the source), and unlike the previous factors discussed, they do not affect the C sinks directly. Therefore, the question here is whether (and if yes, when) the effect of CO2 and light is limiting sink activity (growth) via source activity (blue arrows from source to sink activity via the tree in Fig. 1). In other words, is plant growth C limited and under what conditions? Arguments in favor of C limitation of plant growth are either based on young seedlings and saplings in the forest understory or on individual leaves in dense canopies that are almost always operating below light saturation (Turner, 2001; Lloyd & Farquhar, 2008). Accordingly, CO2 was found to have a stimulatory effect on growth of plants in the understory (Würth et al., 1998; Hättenschwiler & Körner, 2000). Lloyd & Farquhar (2008) inferred that growth of tropical forests is C limited using the relationship between growth and photosynthesis: Np = Gp[1 − φ] where Np is net primary production (new growth), Gp is the average rate of photosynthesis, and φ is the proportion of assimilated C lost via total respiration plus volatile organic C emissions and root C exudation. They argue that higher Gp as a consequence of increasing CO2 concentration or incoming light necessarily leads to enhanced growth. However, we propose that this equation should generally read Np = Gp(Np)[1 − φ(Gp(Np))], thus not be interpreted as a direct causal link between photosynthesis and growth, because φ is a function of Gp, and Np can feedback on photosynthesis, that is, Gp = f(Np). Rewritten in these terms the equation is highly nonlinear and an increase in Gp does not imply a proportional increase in growth. Leaf level light responses of seedling and single leaves are difficult to extrapolate to the forest level, because this would imply that a forest stand behaves like a single leaf or shadowed tree. Light and consequently CO2 limitations of individual leaves, saplings or trees are not a demonstration that the entire forest community operates below its CO2 uptake capacity and even less that C acquisition is limiting forest growth (Körner, 2009; Clark et al., 2013). The Leaf Area Index (LAI) in several ecosystems (e.g. tropical forest, alpine grassland) may be higher than needed to sustain maximal productivity (Amiro et al., 2010; Gough et al., 2013; S. Fatichi, M. J. Zeeman, J. Fuhrer & P. Burlando, unpublished). Many studies suggest that partial (moderate) defoliation hardly affects tree growth or forest productivity (Ericsson et al., 1985; Hoogesteger & Karlsson, 1992; Reich et al., 1993; Kaitaniemi et al., 1999; Volin et al., 2002). A larger than necessary LAI typically has evolutionary rather than physiological reasons: to shade competing neighbors and thus limit their performance; as insurance against herbivory and storm damage; and as an additional option to store nutrients (especially in evergreen trees). Therefore, while light and CO2 may limit growth at the leaf or plant level, they unlikely do so at the landscape (whole forest) level and in the longer term (Leuzinger & Hättenschwiler, 2013). Short-term benefits of extra light (Graham et al., 2003) or elevated CO2 (Norby & Zak, 2011) can only be sustained to the extent a higher growth rate is supported by higher nutrient availability. Current atmospheric CO2 concentration (close to 400 ppm) additionally represents a rather exceptional forcing in the evolutionary context, with the current species having evolved in CO2 concentrations between 180 and 290 ppm (Siegenthaler et al., 2005; Lüthi et al., 2008). This suggests that C availability is at least less limiting nowadays (Körner, 2006). Accordingly, CO2 enrichment experiments with closed forest canopies did not show a sustained stimulation of growth by elevated CO2, except under high nutrient availability (Finzi et al., 2007; Norby et al., 2010; Bader et al., 2013; Sigurdsson et al., 2013). The differential sensitivity of C source and sink activities to water, temperature, and nutrient controls could lead to an imbalance between C supplied by photosynthesis and C used for tissue growth and respiratory costs. A mismatch between these two quantities would be sub-optimal and create a long-term surplus of assimilated C. In normal conditions, this situation is avoided through at least three mechanisms. First, in the short term, acclimation of photosynthesis occurs through negative feedback given by accumulation of starch or higher concentration of sucrose at the leaf level (Paul & Foyer, 2001). However, such a strategy would not be very effective to counteract mid/long-term source–sink imbalance. Second, therefore, in the mid-term, plants use sophisticated mechanisms of C storage at the leaf and whole plant level through accumulation and depletion of NSC (Kozlowski, 1992; Hoch et al., 2003; Gough et al., 2009; Richardson et al., 2013). Fluctuations of NSC are expected to buffer the difference between C supply and demand for timescales from hours to a few years. NSC dynamics are likely to be actively controlled by plants rather than a pure passive deposit of C (Sala et al., 2012). Third, in the long-term, evolutionary processes likely fine-tuned the photosynthesis apparatus (C source) to match long-term investment capacity (C sinks), which is the first to be controlled by environmental limitations. This of source–sink can be summarized as controls (Körner, and the between sinks and which is likely for the hierarchy of controls in the enrichment experiments as well as of water or temperature limitations have the possibility to the between C and For an increase of NSC is typically found in CO2 enrichment Furthermore, atmospheric CO2 almost always enhanced photosynthesis with or no et al., 2005; Bader et al., 2010). However, this typically does not into enhanced growth, in the term and for forests (Leuzinger et al., 2011; Norby & Zak, 2011; Bader et al., 2013). Because the C has to be this imbalance has several the of the & 2013). the extra C is and et al., 2006). Therefore, the of the extra C in CO2 experiments is to root exudates and export to et al., 2012). C assimilation respiration because of the larger and and thus transport respiration & 2000). if root is as a et al., a larger concentration in would more exudates due to the concentration the nutrient limitation through under elevated CO2 et al., 1993; Hättenschwiler & Körner, The use of experiments (e.g. with elevated CO2) is to the C source–sink and therefore the drivers of plant growth, under different environmental there is evidence that tissue growth is mostly under direct control of environmental rather than via the of assimilated C (Fig. and arrows in Fig. which for a of the hierarchy of plant growth control in This implies a from the current C source driven to where C sinks are the Because processes shown in Fig. are strongly and by it is to with either in the short For example, production will decrease with whether the is on growth or whether temperature assimilation and thus growth. However, the processes are fundamentally argue that if these processes and the are not it is unlikely DGVMs will simulate future C storage except under when photosynthesis may as shown in a in Fig. Furthermore, forest and composition are likely to feedback on C assimilation, they will also in long-term C the role of temperature and plant water in growth, of the existing DGVMs we are of include such a even in a simplified or et al., et al., 1997; et al., 2001; Bonan et al., 2003; Sitch et al., 2003; Krinner et al., 2005; et al., 2007; et al., 2009; Clark et al., 2011; et al., 2013; et al., 2013). the of the potential role of nutrients in limiting C from ecosystems et al., nitrogen and more have been in DGVMs et al., 2006; & 2008; & 2010; et al., 2012). However, all these models thermal and water limitations only in to C assimilation but not in to cambial or meristematic The of DGVMs also the that C processes such as photosynthesis (e.g. leaf are while processes of plant growth such as the rate of cell cell expansion, transport or cambial growth (C sink are much to quantify at high (Körner, 2013), even and to a source of et al., 2006; et al., 2013; & 2013). The of C fluxes and the between C and water through stomatal mostly to the of models and of and photosynthesis fluxes (Sellers et al., rather than plant growth. models that et al., to up the of DGVMs, and while they C assimilation, they in plant growth control that is based on observations in controlled but supported by observations in a the key of DGVMs and are almost to of the photosynthesis as for by et Fig. see also Bonan et al., 2012). short few from such DGVMs are typically by C fluxes derived from et al., 2006; et al., However, such only C and not plant growth that is they at show a over a given DGVMs are used to future from C pools and in models may lead us to a because of the Fig. that in models allocation can be controlled to extent by nutrients and environmental For water and light availability can control the differential C allocation to and (Friedlingstein et al., 1998; Krinner et al., 2005). However, this is rather different from having environmental directly control growth because the assimilated C is necessarily to C export to symbionts are mostly and mobile C are only in a of models in a simplified et al., 2005; et al., 2008; & 2010; et al., 2012; et al., 2012; et al., 2013). Therefore, C assimilation is typically to growth, which evidence that between assimilation and growth et al., 2006; Gough et al., 2009; Richardson et al., 2013). This also implies that if more C is assimilated in a under of elevated atmospheric CO2) the vegetation will more with positive feedback to C assimilation through which us with the for the way is to to the existing of DGVMs by direct growth control based on environmental drivers (Leuzinger et al., 2013; see also Fig. we tissue is controlled by environmental the amount of C to be to growth at a given should be of the amount of assimilated C. growth is by the limiting the of all other factors water, and nutrient are this with the that is the C source However, we argue that this is and only under such as in a a modeling the of or of species or plant functional type growth responses to environmental controls (water, temperature, This does not to the current of the could be but no longer the amount of C used for growth. The needed for such will a large of experimental A more to DGVMs on a more realistic of C models that simulate C and water fluxes the nutrient allocation to and from nonstructural C storage and to and root (Fig. This in its was in studies and more have been in models that et al., 2005; et al., 2010; et al., 2012) simulate transport et al., 2002; et al., as well as fluxes and cell growth activities & 2010; et al., 2010). This in the most components where C and water fluxes and are the plant between and in the (Fig. modeling of C assimilation and 1) is in most DGVMs et al., 2011). However, in the water and assimilated C are by leaf water and C and are to buffer the of C assimilation, an almost constant of sucrose to the at the & growth and can be explicitly modeled as a function of cell wall and protein and cell the that nutrients and C are is driven by cell turgor a threshold (Lockhart, which for water limitation to growth. cell typically a Boltzmann–Arrhenius function of temperature (Parent et al., with a temperature most for growth, which represents the thermal limitation to growth. When these two controls are plant growth can be A given by the of water potential and pressure that in also on sucrose concentration, is turgor In to for and et al., can be used for and transport and This will create a coupled system where and in and in and tissue (e.g. are The C is modeled as the of carbohydrates in the and by the of storage This C is then for growth, and for root and export to growth control by nutrients can also be in such a because nutrient through root uptake or can be explicitly and are up both via and actively through the of concentration et al., 2003; & 2012). C can be modeled as a et al., while modeling with is more and from understood which a in providing see While the mechanistic of for example, the leaf water and C or transport is relatively and can be into a of for the of C storage and sink as well as with to storage is a function of plant environmental and For at and C is from storage at the of the to support cambial activity and growth. also C their for example, leaves storage the is not and likely to with plant and plant stress (e.g. such of C allocation are not understood and the of Fig. will a spatial of C allocation with the more parts less C. a of the C sink is and the key for such a that a of the in Fig. is not given the limited we argue that current is for modeling most of the also that such components may to be in DGVMs and would a large of However, its can the for of environmental control of growth and of mechanistic C allocation schemes that could be into A where plant transport and tissue expansion are using well and among as was for photosynthesis is likely to way of modeling plant growth. a can also the of on plant C storage and sink which is fundamental for increasing and long-term scenarios for the C The of photosynthetic and the of mechanistic models of C assimilation that simulate observations have the of DGVMs, which have few over the identified 2006; et al., the of models mostly However, current physiological of tissue growth and expansion is in to it is in Current scenarios the between climate and vegetation are likely affected by a of plant growth and are therefore argue that direct environmental controls water and nutrient on tissue expansion and meristematic activity are fundamental than on the of C because they mostly limiting to plant growth photosynthesis Because C can only be to the extent other nutrients as well as water availability and temperature these drivers of growth will be over C assimilation This in of plant growth drivers has for the of terrestrial C fluxes and storage in the The of the in DGVMs will be but fundamental for future of the C In to this we two of different to growth in current DGVMs to growth limiting to mechanistic of plant transport and tissue expansion as was for photosynthesis While additional may be to of the we argue that there is evidence to the of the of current DGVMs, from to a plant growth. The three and the for on an earlier of this with are also